HBO originally launched Max at a time when almost every cable TV conglomerate was releasing their own streaming service, to compete with Netflix and Amazon Prime Video. In Warner Bros case, it had...

About this dataset

Context

The dataset tabulates the population of Max by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Max across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 54.93% of total population being female. 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.

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. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Max is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Max 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 Max Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Max by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Max. The dataset can be utilized to understand the population distribution of Max by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Max. 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 Max.

Key observations

Largest age group (population): Male # 50-54 years (23) | Female # 5-9 years (27). 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 Max population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Max is shown in the following column.
• Population (Female): The female population in the Max 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 Max 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 Max Population by Gender. You can refer the same here

2016-2019. This dataset is a de-identified summary table of prevalence rates for vision and eye health data indicators from the Medicaid Analytic eXtract (MAX) data. Medicaid MAX are a set of de-identified person-level data files with information on Medicaid eligibility, service utilization, diagnoses, and payments. The MAX data contain a convenience sample of claims processed by Medicaid and Children’s Health Insurance Program (CHIP) fee for service and managed care plans. Not all states are included in MAX in all years, and as of November 2019, 2014 data is the latest available. Prevalence estimates are stratified by all available combinations of age group, gender, and state. Detailed information on VEHSS Medicare analyses can be found on the VEHSS Medicaid MAX webpage (cdc.gov/visionhealth/vehss/data/claims/medicaid.html). Information on available Medicare claims data can be found on the ResDac website (www.resdac.org). The VEHSS Medicaid MAX dataset was last updated May 2023.

Overview

Streaming Services Statistics: Streaming services have transformed the entertainment landscape, revolutionizing how people consume content.

The advent of high-speed internet and the proliferation of smart devices have fueled the growth of these platforms, offering a wide array of movies, TV shows, music, and more, at the viewers' convenience.

This introduction provides an overview of key statistics that shed light on the impact, trends, and challenges within the streaming industry.

US Equities Max is the ultimate option for every non-professional user, allowing individuals to access the most precise price information in the United States. This dataset is also ideal for companies engaged in trading, building an app, or any other projects that require unsacrificed US market data.

US Equities Max is a consolidated dataset that provides all level-1 price information from all exchanges in the United States.

This feed is the quickest option for getting data over the cloud, as it is disseminated in real-time with an average latency of 100 milliseconds. It contains aggregated time series (OHLCV) ranging from 1-minute to 1-month intervals, raw trades and quotes, as well as snapshots with price summaries. The extensive reference data includes lists of tickers, markets, and far more. In addition to the REST API, the majority of the data can be streamed via WebSocket.

To create this dataset, Finazon receives raw prices from UTP and CTA, also known as SIPs. Prices are further aggregated using a complex set of rules, which results in an unbiased dataset that is built upon every single executed trade in the country. Data is adjusted for splits and dividends, allowing you to be certain about price accuracy when working with the US Equities Max dataset.

About this dataset

Context

The dataset tabulates the Max median household income by race. The dataset can be utilized to understand the racial distribution of Max income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Max, ND median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Max, ND (2021, in 2022 inflation-adjusted dollars)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Max median household income by race. You can refer the same here

Wilhelm Busch "Max und Moritz" Dataset

Welcome to the Wilhelm Busch "Max und Moritz" Dataset, a curated collection of 73 public domain images from the classic German children's book "Max und Moritz". This dataset has been enhanced with GPT-Vision generated captions and is ready for training AI models.

  Captain: The AI platform that evolves to your needs

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  Dataset Overview

Content: The dataset contains 73… See the full description on the dataset page: https://huggingface.co/datasets/Blib-la/max_und_moritz_wilhelm_busch_dataset.

NOTE: This dataset is now outdated. Please see https://doi.org/10.11583/DTU.17091101 for the updated version with many more problems.

This is a collection of min-cut/max-flow problem instances that can be used for benchmarking min-cut/max-flow algorithms. The collection is released in companionship with the paper:

• Jensen et al., "Review of Serial and Parallel Min-Cut/Max-Flow Algorithms for Computer Vision", T-PAMI, 2022.

The problem instances are collected from a wide selection of sources to be as representative as possible. Specifically, this collection contains:

• Most of the problem instances (some are unavailable due to dead links) published by the University of Waterloo: https://vision.cs.uwaterloo.ca/data/maxflow
• Super-resolution, texture restoration, deconvolution, decision tree field (DTF) and automatic labeling environment from Verma & Batra, "MaxFlow Revisited: An Empirical Comparison of Maxflow Algorithms for Dense Vision Problems", 2012, BMVC
• Sparse Layered Graph instances from Jeppesen et al., "Sparse Layered Graphs for Multi-Object Segmentation", 2020, CVPR
• Multi-object surface fitting from Jensen et al., "Multi-Object Graph-Based Segmentation With Non-Overlapping Surfaces", 2020, CVPRW

The reason for releasing this collection is to provide a single place to download all datasets used in our paper (and various previous paper) instead of having to scavenge from multiple sources. Furthermore, several of the problem instances typically used for benchmarking min-cut/max-flow algorithms are no longer available at their original locations and may be difficult to find. By storing the data on Zenodo with a dedicated DOI we hope to avoid this. For license information, see below.

Files and formats

We provide all problem instances in two file formats: DIMACS and a custom binary format. Both are described below. Each file has been zipped, and similar files have then been grouped into their own zip file (i.e. it is a zip of zips). DIMACS files have been prefixed with `dimacs_` and binary files have been prefixed with `bin_`.

DIMACS

All problem instances are available in DIMACS format (explained here: http://lpsolve.sourceforge.net/5.5/DIMACS_maxf.htm).

For the larger problem instances, we have also published a partition of the graph nodes into blocks for block-based parallel min-cut/max-flow. The partition matches the one used in the companion review paper (Jensen et al., 2021). For a problem instance with filename `

4
0 0 1 1 2 2 3 3 0 0 1 1 2 2 3 3 0 0 1 1 2 2 3 3 0 0 1 1 2 2 3 3

Binary files

While DIMACS has the advantage of being human-readable, storing everything as text requires a lot of space. This makes the files unnecessarily large and slow to parse. To overcome this, we also release all problem instances in a simple binary storage format. We have two formats: one for graphs and one for quadratic pseudo-boolean optimization (QPBO) problems. Code to convert to/from DIMACS is also available at: https://www.doi.org/10.5281/zenodo.4903946 or https://github.com/patmjen/maxflow_algorithms.

Binary BK (`.bbk`) files are for storing normal graphs for min-cut/max-flow. They closely follow the internal storage format used in the original implementation of the Boykov-Kolmogorov algorithm, meaning that terminal arcs are stored in a separate list from normal neighbor arcs. The format is:

Uncompressed:

Header: (3 x uint8) 'BBQ'
Types codes: (2 x uint8) captype, tcaptype
Sizes: (3 x uint64) num_nodes, num_terminal_arcs, num_neighbor_arcs
Terminal arcs: (num_terminal_arcs x BkTermArc)
Neighbor arcs: (num_neighbor_arcs x BkNborArc)

Compressed (using Google's snappy: https://github.com/google/snappy):

Header: (3 x uint8) 'bbq'
Types codes: (2 x uint8) captype, tcaptype
Sizes: (3 x uint64) num_nodes, num_terminal_arcs, num_neighbor_arcs
Terminal arcs: (1 x uint64) compressed_bytes_1
        (compressed_bytes_1 x uint8) compressed num_terminal_arcs x BkTermArc
Neighbor arcs: (1 x uint64) compressed_bytes_2
        (compressed_bytes_2 x uint8) compressed num_neighbor_arcs x BkNborArc

Where:

/** Enum for switching over POD types. */
enum TypeCode : uint8_t {
  TYPE_UINT8,
  TYPE_INT8,
  TYPE_UINT16,
  TYPE_INT16,
  TYPE_UINT32,
  TYPE_INT32,
  TYPE_UINT64,
  TYPE_INT64,
  TYPE_FLOAT,
  TYPE_DOUBLE,
  TYPE_INVALID = 0xFF
};

/** Terminal arc with source and sink capacity for given node. */
template 

Binary QPBO (`.bq`) files are for storing QPBO problems. Unary and binary terms are stored in separate lists. The format is:

Uncompressed:

Header: (5 x uint8) 'BQPBO'
Types codes: (1 x uint8) captype
Sizes: (3 x uint64) num_nodes, num_unary_terms, num_binary_terms
Unary arcs: (num_unary_terms x BkUnaryTerm)
Binary arcs: (num_binary_terms x BkBinaryTerm)

Compressed (using Google's snappy: https://github.com/google/snappy):

Header: (5 x uint8) 'bqpbo'
Types codes: (1 x uint8) captype
Sizes: (3 x uint64) num_nodes, num_unary_terms, num_binary_terms
Unary terms: (1 x uint64) compressed_bytes_1
       (compressed_bytes_1 x uint8) compressed num_unary_terms x BkUnaryTerm
Binary terms: (1 x uint64) compressed_bytes_2
       (compressed_bytes_2 x uint8) compressed num_binary_terms x BkBinaryTerm

Where:

/** Enum for switching over POD types. */
enum TypeCode : uint8_t {
  TYPE_UINT8,
  TYPE_INT8,
  TYPE_UINT16,
  TYPE_INT16,
  TYPE_UINT32,
  TYPE_INT32,
  TYPE_UINT64,
  TYPE_INT64,
  TYPE_FLOAT,
  TYPE_DOUBLE,
  TYPE_INVALID = 0xFF
};

/** Unary term */
template 

Block (`.blk`) files are for storing a partition of the graph nodes into disjoint blocks. The format is:

Nodes: uint64_t num_nodes
Blocks: uint16_t num_blocks
Data: (num_nodes x uint16_t) node_blocks

We do not claim ownership over the problem instances from the University of Waterloo and those from (Verma and Batra, 2012). Please contact the original sources for additional information. We publish the datasets from (Jeppesen et al., 2020) and (Jensen et al., 2020) under the Creative Commons Attribution 4.0 International (CC BY 4.0), see https://creativecommons.org/licenses/by/4.0/. The specific files are (not counting their `dimacs_`/`bin_` prefix):

University of Waterloo (consult original sources)

• adhead.zip
• babyface.zip
• bone.zip
• bone_sub.zip
• liver.zip
• BL06.zip
• BVZ.zip
• LB07.zip
• KZ2.zip

Verma & Batra, 2012 (consult original sources):

• ale.zip
• dtf.zip
• deconv.zip
• super_res.zip
• texture.zip

Jeppesen et al., 2020 (CC BY 4.0):

• NT32_tomo3.zip
• NT32_tomo3_216.zip

Jensen et al., 2020 (CC BY 4.0):

• cells.zip
• foam.zip
• simcells.zip

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Max. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Max. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Max, the median household income stands at $86,250 for householders within the 45 to 64 years age group, followed by $61,563 for the 25 to 44 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $60,625.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific 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 Max median household income by age. You can refer the same here

Data and code for "Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions" CONTEXT: Soybean (Glycine max (L.) Merr.) planting has increased in central and western North Dakota despite frequent drought occurrences that limit productivity. Soybean plants need high photosynthetic and transpiration rates to be productive, but they also need high water use efficiency when water is limited. Retaining crop residues and including cover crops in crop rotations are management strategies that could improve soybean drought resilience in the northern Great Plains. OBJECTIVE: We aimed to examine how a management practice that included cover crops and residue retention impacts agronomic, ecosystem water and carbon dioxide flux, and canopy-scale physiological attributes of soybeans in the northern Great Plains under drought conditions. METHODS: We compared two soybean fields over two years with business-as-usual and aspirational management that included residue retention and cover crops during a drought year. This comparison was based on yield, aboveground biomass, Phenocam images, and fluxes from eddy covariance and ancillary measurements. These measurements were used to derive meteorological, physical, and physiological attributes with the ‘big leaf’ framework. RESULTS: Soybean yields were 29% higher under drought conditions in the field managed in a system that included cover crops and residue retention. This yield increase was caused by extending the maturity phenophase by 5 days, increasing agronomic and intrinsic water use efficiency by 27% and 33%, respectively, increasing water uptake, and increasing the rubisco-limited photosynthetic capacity (Vcmax25) by 42%. CONCLUSIONS: The inclusion of cover crops and residue retention into a cropping system improved soybean productivity because of differences in water use, phenology timing, and photosynthetic capacity. IMPLICATIONS: These results suggest that farmers can improve soybean productivity and yield stability by incorporating cover crops and residue retention into their management practices because these practices allow soybean plants to shift to a more aggressive water uptake strategy. Data Half_Hourly.csv: Half hour data from eddy covariance towers Management.csv: data about field management Phenocamdata.csv: The output of 1_phenocam.Rmd code Predicted_Height_LAI.csv: The output of 3_Inferring_LAI_and_Height.Rmd Vegetation.csv: biomass and yield data Code 1_phenocam.rmd: Code to download Phenocam data and identify phenophase transition dates. 2_Daily_CO2_Water_Fluxes.Rmd: Code to analyze daily carbon and water fluxes (Figure 1, 2 3 and Table 2). 3_Inferring_LAI_and_Height.Rmd: Code to calculate the predicted LAI and height for each day. The output is used in the big-leaf framework. 4_Big_Leaf.Rmd: Code for the big-leaf ecophysiology estimates (Figure 4, 5 and 6; Table 3 and 4). 4_Data_Dictionary_Variables: Code to identify the data dictionary variables.

Parkinson's data set

This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to "status" column which is set to 0 for healthy and 1 for PD.

The data is in ASCII CSV format. The rows of the CSV file contain an instance corresponding to one voice recording. There are around six recordings per patient, the name of the patient is identified in the first column. For further information or to pass on comments, please contact Max Little (littlem '@' robots.ox.ac.uk).

Further details are contained in the following reference -- if you use this dataset, please cite: Max A. Little, Patrick E. McSharry, Eric J. Hunter, Lorraine O. Ramig (2008), 'Suitability of dysphonia measurements for telemonitoring of Parkinson's disease', IEEE Transactions on Biomedical Engineering (to appear).

This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.

Citation:

Little,Max. (2008). Parkinsons. UCI Machine Learning Repository. https://doi.org/10.24432/C59C74.

Matrix column entries (attributes):

name - ASCII subject name and recording number MDVP:Fo(Hz) - Average vocal fundamental frequency MDVP:Fhi(Hz) - Maximum vocal fundamental frequency MDVP:Flo(Hz) - Minimum vocal fundamental frequency Five measures of variation in Frequency MDVP:Jitter(%) - Percentage of cycle-to-cycle variability of the period duration MDVP:Jitter(Abs) - Absolute value of cycle-to-cycle variability of the period duration MDVP:RAP - Relative measure of the pitch disturbance MDVP:PPQ - Pitch perturbation quotient Jitter:DDP - Average absolute difference of differences between jitter cycles Six measures of variation in amplitude MDVP:Shimmer - Variations in the voice amplitdue MDVP:Shimmer(dB) - Variations in the voice amplitdue in dB Shimmer:APQ3 - Three point amplitude perturbation quotient measured against the average of the three amplitude Shimmer:APQ5 - Five point amplitude perturbation quotient measured against the average of the three amplitude MDVP:APQ - Amplitude perturbation quotient from MDVP Shimmer:DDA - Average absolute difference between the amplitudes of consecutive periods Two measures of ratio of noise to tonal components in the voice NHR - Noise-to-harmonics Ratio and HNR - Harmonics-to-noise Ratio status - Health status of the subject (one) - Parkinson's, (zero) - healthy Two nonlinear dynamical complexity measures RPDE - Recurrence period density entropy D2 - correlation dimension DFA - Signal fractal scaling exponent Three nonlinear measures of fundamental frequency variation spread1 - discrete probability distribution of occurrence of relative semitone variations spread2 - Three nonlinear measures of fundamental frequency variation PPE - Entropy of the discrete probability distribution of occurrence of relative semitone variations

By Bastian Herre, Pablo Arriagada, Esteban Ortiz-Ospina, Hannah Ritchie, Joe Hasell and Max Roser.

About dataset:

Women’s rights are human rights that all women have. But in practice, these rights are often not protected to the same extent as the rights of men.

Among others, women’s rights include: physical integrity rights, such as being free from violence and making choices over their own body; social rights, such as going to school and participating in public life; economic rights, such as owning property, working a job of their choice, and being paid equally for it; and political rights, such as voting for and holding public office.

The protection of these rights allows women to live the lives they want and to thrive in them.

On this page, you can find data on how the protection of women’s rights has changed over time, and how it differs across countries.

There are 6 dataset in here.

1- Female to male ratio of time devoted to unpaid care work. 2- Share of women in top income groups atkinson casarico voitchovsky 2018. 3- Ratio of female to male labor force participation rates ilo wdi. 4- Female to male ratio of time devoted to unpaid care work. 5- Maternal mortality 6- Gender gap in average wages ilo

In each one, there are some topics and variables that we can analysis and visualize them.

• Update Frequency: Weekly

Data from this dataset can be downloaded/accessed through this dataset page and Kaggle's API.

Context

Severe weather is defined as a destructive storm or weather. It is usually applied to local, intense, often damaging storms such as thunderstorms, hail storms, and tornadoes, but it can also describe more widespread events such as tropical systems, blizzards, nor'easters, and derechos.

The Severe Weather Data Inventory (SWDI) is an integrated database of severe weather records for the United States. The records in SWDI come from a variety of sources in the NCDC archive. SWDI provides the ability to search through all of these data to find records covering a particular time period and geographic region, and to download the results of your search in a variety of formats. The formats currently supported are Shapefile (for GIS), KMZ (for Google Earth), CSV (comma-separated), and XML.

Content

The current data layers in SWDI are:
- Filtered Storm Cells (Max Reflectivity >= 45 dBZ) from NEXRAD (Level-III Storm Structure Product)
- All Storm Cells from NEXRAD (Level-III Storm Structure Product)
- Filtered Hail Signatures (Max Size > 0 and Probability = 100%) from NEXRAD (Level-III Hail Product)
- All Hail Signatures from NEXRAD (Level-III Hail Product)
- Mesocyclone Signatures from NEXRAD (Level-III Meso Product)
- Digital Mesocyclone Detection Algorithm from NEXRAD (Level-III MDA Product)
- Tornado Signatures from NEXRAD (Level-III TVS Product)
- Preliminary Local Storm Reports from the NOAA National Weather Service
- Lightning Strikes from Vaisala NLDN

Disclaimer:
SWDI provides a uniform way to access data from a variety of sources, but it does not provide any additional quality control beyond the processing which took place when the data were archived. The data sources in SWDI will not provide complete severe weather coverage of a geographic region or time period, due to a number of factors (eg, reports for a location or time period not provided to NOAA). The absence of SWDI data for a particular location and time should not be interpreted as an indication that no severe weather occurred at that time and location. Furthermore, much of the data in SWDI is automatically derived from radar data and represents probable conditions for an event, rather than a confirmed occurrence.

Acknowledgements

Dataset Source: NOAA. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Cover photo by NASA on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset is about book subjects. It has 2 rows and is filtered where the books is Poppy and Max and too many muffins. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The Max & Fran Hommersand Algae Herbarium is curated by the University of North Carolina at Chapel Hill Herbarium (NCU). Note that algae collected before 1995 by Hommersand were placed in formalin. Gabrielson's non-coralline specimens are a mix; if pressed fresh this is noted on the specimen label. Gabrielson's coralline specimens were not put in formalin unless noted on the label. NCU also curates vascular plants, lichens, fungi, bryophytes, & plant fossils. NCU, located in the center of the UNC-CH campus, welcomes visitors & researchers; contact Curator for information on hours & parking. STATEMENT ON OFFENSIVE CONTENT ON SPECIMEN LABELS: Collection records at NCU may contain language that reflects historical place or taxon names in an original form that is no longer acceptable or appropriate in an inclusive environment. Because NCU preserves data in their original form to retain authenticity and facilitate research, we have chosen to facilitate conversations and are committed to address the problem of racial, derogatory and demeaning language that may be found in our database. Insensitive or offensive language is not condoned by NCU. We recognize the land and sovereignty of Native & Indigenous nations in Chapel Hill, in North Carolina, in North America, and across the world. The North Carolina Botanical Garden and the North Carolina Botanical Garden Foundation acknowledge that the story told about the history of the land we steward has been incomplete. These lands were home to multiple tribes & the ancestors of the Occaneechi Band of the Saponi Nation persist locally to this day. We recognize that at least one of the adjacent lands we steward, Mason Farm Biological Reserve, was first cleared, cultivated, & worked by Native Americans & later by African enslaved people. We invite you to reflect on our individual & community roles in knowing important & untold stories about the land we each steward.

Over the past several decades, many climate datasets have been exchanged directly between the principal climate data centers of the United States (NOAA's National Climatic Data Center (NCDC)) and the former-USSR/Russia (All-Russian Research Institute for Hydrometeorological Information-World Data Center (RIHMI-WDC)). This data exchange has its roots in a bilateral initiative known as the Agreement on Protection of the Environment (Tatusko 1990). CDIAC has partnered with NCDC and RIHMI-WDC since the early 1990s to help make former-USSR climate datasets available to the public. The first former-USSR daily temperature and precipitation dataset released by CDIAC was initially created within the framework of the international cooperation between RIHMI-WDC and CDIAC and was published by CDIAC as NDP-040, consisting of data from 223 stations over the former USSR whose data were published in USSR Meteorological Monthly (Part 1: Daily Data). The database presented here consists of records from 518 Russian stations (excluding the former-USSR stations outside the Russian territory contained in NDP-040), for the most part extending through 2010. Records not extending through 2010 result from stations having closed or else their data were not published in Meteorological Monthly of CIS Stations (Part 1: Daily Data). The database was created from the digital media of the State Data Holding. The station inventory was arrived at using (a) the list of Roshydromet stations that are included in the Global Climate Observation Network (this list was approved by the Head of Roshydromet on 25 March 2004) and (b) the list of Roshydromet benchmark meteorological stations prepared by V.I. Kodratyuk, Head of the Department at Voeikov Main Geophysical Observatory. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/russia_daily518.html

This is our GTFS Realtime API. More information about the GTFS Realtime feed specification can be found here: https://developers.google.com/transit/gtfs-realtime De Lijn does apply limits to its open data. On request and when justified we can increase these limits per subscription Max 864000 calls per product per day Max 6000 calls per product per minute We have some more non disclosed measures to ensure API stability We do not provide SLAs. Data quality The quality of the data that is disclosed is equal to the quality that is sufficient for internal use of the data (within the services of the Flemish or local government).

Please use the MESINESP2 corpus (the second edition of the shared-task) since it has a higher level of curation, quality and is organized by document type (scientific articles, patents and clinical trials).

INTRODUCTION:

The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) training set has a total of 369,368 records.

The training dataset contains all records from LILACS and IBECS databases at the Virtual Health Library (VHL) with a non-empty abstract written in Spanish. The URL used to retrieve records is as follows: http://pesquisa.bvsalud.org/portal/?output=xml&lang=es&sort=YEAR_DESC&format=abstract&filter[db][]=LILACS&filter[db][]=IBECS&q=&index=tw&

We have filtered out empty abstracts and non-Spanish abstracts.

The training dataset was crawled on 10/22/2019. This means that the data is a snapshot of that moment and that may change over time. In fact, it is very likely that the data will undergo minor changes as the different databases that make up LILACS and IBECS may add or modify the indexes.

ZIP STRUCTURE:

The training data sets contain 369,368 records from 26,609 different journals. Two different data sets are distributed as described below:

• Original Train set with 369,368 records that also include the qualifiers, as retrieved from VHL.
• Pre-processed Train set with the 318,658 records with at least one DeCS code and with no qualifiers.

STATISTICS:

Abstracts’ length (measured in characters) Min: 12 Avg: 1140.41 Median: 1094 Max: 9428

Number of DeCS codes per file Min: 1 Avg: 8.12 Median: 7 Max: 53

CORPUS FORMAT:

The training data sets are distributed as a JSON file with the following format:

{ "articles": [ { "id": "Id of the article", "title": "Title of the article", "abstractText": "Content of the abstract", "journal": "Name of the journal", "year": 2018, "db": "Name of the database", "decsCodes": [ "code1", "code2", "code3" ] } ] }

Note that the decsCodes field lists the DeCs Ids assigned to a record in the source data. Since the original XML data contain descriptors (no codes), we provide a DeCs conversion table (https://temu.bsc.es/mesinesp/wp-content/uploads/2019/12/DeCS.2019.v5.tsv.zip) with:

• DeCs codes
• Preferred descriptor (the label used in the European DeCs 2019 set)
• List of synonyms (the descriptors and synonyms from both European and Latin Spanish DeCs 2019 data sets, separated by pipes)

For more details on the Latin and European Spanish DeCs codes see: http://decs.bvs.br and http://decses.bvsalud.org/ respectively.

Please, cite: Krallinger M, Krithara A, Nentidis A, Paliouras G, Villegas M. BioASQ at CLEF2020: Large-Scale Biomedical Semantic Indexing and Question Answering. InEuropean Conference on Information Retrieval 2020 Apr 14 (pp. 550-556). Springer, Cham.

Copyright (c) 2020 Secretaría de Estado de Digitalización e Inteligencia Artificial

This data set contains data from the University of Alabama Huntsville Mobile Alabama X-band Radar (MAX). It was collected during intense observation periods (IOPs) between 1 March and 1 May 2016 for the Verification of the Origins of Rotation in Tornadoes EXperiment-Southeast (VORTEX-SE) 2016 Field Campaign. Preliminary field corrections were made, however the data is considered raw and has not undergone quality control. The data is provided in both raw file format and converted to the Universal Format (Common Doppler Radar Exchange Format). For more information about the instrument, data format, and quality control procedure, see the included documentation pdf.