Publicly accessible open spaces provide valuable opportunities for people to exercise, play, socialize, and build community. People are more likely to use public open spaces that are close (ideally within walking distance) to their homes, and larger open spaces often provide more amenities. To assess the potential benefit of creating new open space in the southeast US, we identified areas without access to open space within a certain distance category (in this case, 3 miles). Then, for each 30-meter pixel in the study area, we then totaled the number of people within 3 miles who do not currently have access to open space within that distance. This represents the number of people who would benefit from new open space created on that pixel.

Publicly accessible open spaces provide valuable opportunities for people to exercise, play, socialize, and build community. People are more likely to use public open spaces that are close (ideally within walking distance) to their homes, and larger open spaces often provide more amenities. To assess the potential benefit of creating new open space in the southeast US, we identified areas without access to open space within a certain distance category (in this case, 10 miles). Then, for each 30-meter pixel in the study area, we then totaled the number of people within 10 miles who do not currently have access to open space within that distance. This represents the number of people who would benefit from new open space created on that pixel.

Context Who is going to win the space race? Billionaires and their missions. What do the people think?

Content This dataset contains tweets from Twitter regarding the recent Space Race involving Billionaires like Elon Musk, Jeff Bezos and Richard Branson and their respective Companies - SpaceX, Blue Origin and Virgin Galactic.

Use the dataset to perform Sentimental Analysis and come to an conclusion what do the people think about Billionaires and their Space related Companies. Find out who is winning the Space Race 🚀….

License

CC0

Original Data Source: The Space Race Tweets

Publicly accessible open spaces provide valuable opportunities for people to exercise, play, socialize, and build community. People are more likely to use public open spaces that are close (ideally within walking distance) to their homes, and larger open spaces often provide more amenities. To assess the potential benefit of creating new open space in the southeast US, we identified areas without access to open space within a certain distance category (in this case, 0.5 miles). Then, for each 30-meter pixel in the study area, we then totaled the number of people within 0.5 miles who do not currently have access to open space within that distance. This represents the number of people who would benefit from new open space created on that pixel.

Here are a few use cases for this project:

1. Crowd Monitoring: The model could be used by security departments to monitor large crowds in public spaces, like parks, subway stations, and stadiums. With its ability to identify individual people even in crowded situations, it assists in analyzing crowd movement patterns or spotting suspicious activities.

2. Social Distancing Compliance: Amid the COVID-19 pandemic, this model can be instrumental in enforcing social distancing norms. It can be used in malls, offices, schools, or restaurants to identify the concentration of people and ensure compliance with health protocols.

3. Smart Home Security: The model can be implemented in home security systems to identify the presence of humans in certain areas of the house, alerting homeowners to possible intruders.

4. Occupancy Control Systems: Establishments like libraries, gyms, or coworking spaces can use this model to monitor and regulate the number of users in a specific area at a given time, supporting occupancy management or limit enforcement.

5. Public Space Design Research: By identifying the density and placement of people in places like parks or plazas, city planners and urban designers might use this model to obtain usage data, helping them design more user-friendly and efficient public spaces.

This dataset was built for training and validating terrain classification models for Mars, which may be useful in future autonomous rover efforts. It consists of ~326K semantic segmentation full image labels on 35K images from Curiosity, Opportunity, and Spirit rovers, collected through crowdsourcing. Each image was labeled by 10 people to ensure greater quality and agreement of the crowdsourced labels. It also includes ~1.5K validation labels annotated by the rover planners and scientists from NASA’s MSL (Mars Science Laboratory) mission, which operates the Curiosity rover, and MER (Mars Exploration Rovers) mission, which operated the Spirit and Opportunity rovers.

The image is a simulated natural color rendition showing how North Dakota would appear to the naked eye from orbit. This view of North Dakota from space was acquired by the Landsat 7 satellite between July 1999 and September 2002. The addition of digital terrain information emphasizes the perception of depth by adding shadows in areas of rugged terrain such as the badlands of western North Dakota.

This satellite image mosaic was provided by the U.S. Geological Survey, Earth Resources Observations Systems (EROS) Data Center in Sioux Falls, SD. This image is brought to you courtesy of the Northern Great Plains Center for People and the Environment at the University of North Dakota and it is affiliated research and educational organizations, the Upper Midwest Aerospace Consortium (UMAC) and the graduate program in Earth System Science and Policy.

The satellite circles the Earth at an altitude of approximately 438 miles (705 kilometers) in a nearly pole-to-pole (98-degree inclination) orbit and crosses the equator traveling north to south at about 10 a.m. local time each orbit.

Constraints:
Not to be used for navigation, for informational purposes only. See full disclaimer for more information.

SUMMARYThe area (in hectares) of publicly accessible blue- and green-space per 1000 population within each Middle Layer Super Output Area (MSOA).This dataset was produced to identify how much green/blue space (areas with greenery and/or inland water) people have to opportunity to experience within each MSOA. This includes land that the public can directly access and land they are able to walk/cycle/etc. immediately adjacent to.The area of accessible green/blue space, as a percentage of the total area of the MSOA, is also given.ANALYSIS METHODOLOGYThe following were identified as ‘accessible’ blue and green spaces:A) CRoW Open Access LandB) Doorstep GreensC) Open Greenspace (features described as a ‘play space’, ‘playing field’ or ‘public park or garden’)D) Local Nature ReservesE) Millennium GreensF) National Nature ReservesG) ‘Green’ and ‘blue’ land types – inland water, tidal water, woodland, foreshore, countryside/fields – and Open Greenspace types not identified in Point C that are immediately adjacent to*:G1) Coastal Path RoutesG2) National Cycle Network (traffic-free routes only)G3) National Forest Estate recreation routesG4) National TrailsG5) Path networks within built up areas (OS MasterMap Highways Network Paths)G6) Public Rights of Way*Features G1-6 were buffered by 20 m. All land described in Point G that fell within those 20 m buffers was extracted. Of those areas, any land that was >3m away from features G1-6 in its entirety was assumed to have non-green/blue features between the public path/route/trail and it, and was therefore removed.Population statistics for each MSOA were combined with the statistics re. the area of accessible green/blue space, to calculate the area of accessible green-blue space per 1000 population.LIMITATIONS1. Access to beaches and the sea could not be factored into the analysis, and should be considered when interpreting the results for MSOAs on the coastline.2. This dataset highlights were there are opportunities for the public to experience green/blue space. It does not (and could not) determine the level of accessibility for users with differing levels of mobility.3. Public Right of Way (PRoW) data was not available for the whole of England. While some gaps in the data will have been partially filled in by the OS MasterMap Highways Network Paths dataset, due to overlap between the two, some gaps will still remain. As such, this dataset should be viewed in combination with the ‘Area of accessible green and blue space per 1000 population (England): Missing data’ dataset in ArcGIS Online or, if using the data in desktop GIS, the ‘NoProwData’ field should be consulted. The area of accessible green/blue space in those areas could be slightly under represented in this dataset. TO BE VIEWED IN COMBINATION WITH:Area of accessible green and blue space per 1000 population (England): Missing dataDATA SOURCESCoastal Path Routes; CRoW Act 2000 - Access Layer; Doorstep Greens: Local Nature Reserves; Millennium Greens; National Nature Reserves; National Trails: © Natural England copyright 2021. Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0. Available from the Natural England Open Data Geoportal.OS Open Greenspace; OS VectorMap® District: Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0.OS MasterMap Highways Network Paths: Contains Ordnance Survey data © Crown copyright and database right 2021. National Cycle Network © Sustrans 2021, licensed under the Open Government Licence v3.0.National Forest Estate Recreation Routes: © Forestry Commission 2016.Population data: Mid-2019 (June 30) Population Estimates for Middle Layer Super Output Areas in England and Wales. © Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2020.MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.Public Rights of Way: Copyright of various local authorities.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Produced using data: © Natural England copyright 2021. Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0.; © Sustrans 2021, licensed under the Open Government Licence v3.0.; © Forestry Commission 2016.; © Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2020.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Abstract

The Opportunity Atlas uses anonymous data following 20 million Americans from childhood to their mid-thirties to answer this question of which neighborhoods in America offer children the best chance at a better life than their parents.

Context

Sign Language is a communication language just like any other language which is used among deaf community. This dataset is a complete set of gestures which are used in sign language and can be used by other normal people for better understanding of the sign language gestures .

Content

The dataset consists of 37 different hand sign gestures which includes A-Z alphabet gestures, 0-9 number gestures and also a gesture for space which means how the deaf or dumb people represent space between two letter or two words while communicating. The dataset has two parts, that is two folders (1)-Gesture Image Data - which consists of the colored images of the hands for different gestures. Each gesture image is of size 50X50 and is in its specified folder name that is A-Z folders consists of A-Z gestures images and 0-9 folders consists of 0-9 gestures respectively, '_' folder consists of images of the gesture for space. Each gesture has 1500 images, so all together there are 37 gestures which means there 55,500 images for all gestures in the 1st folder and in the 2nd folder that is (2)-Gesture Image Pre-Processed Data which has the same number of folders and same number of images that is 55,500. The difference here is these images are threshold binary converted images for training and testing purpose. Convolutional Neural Network is well suited for this dataset for model training purpose and gesture prediction.

Acknowledgements

I wouldn't be here without the help of others. As this dataset is being created with the help of references of the work done on sign language in data science and also references from the work done on image processing.

Context

Space Apps Moscow was held on April 29th & 30th. Thank you to the 175 people who joined the International Space Apps Challenge at this location!

Content

The dataset contains such columns as: "wind direction", "wind speed", "humidity" and temperature. The response parameter that is to be predicted is: "Solar_radiation". It contains measurements for the past 4 months and you have to predict the level of solar radiation. Just imagine that you've got solar energy batteries and you want to know will it be reasonable to use them in future?

Acknowledgements

Thanks NASA for the dataset.

Inspiration

Predict the level of solar radiation. Here are some intersecting dependences that i have figured out: 1. Humidity & Solar_radiation. 2.Temeperature & Solar_radiation.

The best result of accuracy I could get using cross-validation was only 55%.

Publicly accessible open spaces provide valuable opportunities for people to exercise, play, socialize, and build community. People are more likely to use public open spaces that are close (ideally within walking distance) to their homes. To assess the spatial distribution of access to open space for recreation in the southeastern United States, we constructed an index of open space access based on the size of the largest publicly accessible open space within 10 miles of each point on the landscape, using three distance categories to represent whether people can reach the open spaces by walking (within 0.5 mile), via a short drive (within 3 miles), or via a longer drive (within 10 miles). Using the open space access index, we identified regional priority areas at the county scale based on the number of people who would have increased access to open space (within the three distance categories) if new open space were created within those areas.

Over the last ten years, social media has become a crucial data source for businesses and researchers, providing a space where people can express their opinions and emotions. To analyze this data and classify emotions and their polarity in texts, natural language processing (NLP) techniques such as emotion analysis (EA) and sentiment analysis (SA) are employed. However, the effectiveness of these tasks using machine learning (ML) and deep learning (DL) methods depends on large labeled datasets, which are scarce in languages like Spanish. To address this challenge, researchers use data augmentation (DA) techniques to artificially expand small datasets. This study aims to investigate whether DA techniques can improve classification results using ML and DL algorithms for sentiment and emotion analysis of Spanish texts. Various text manipulation techniques were applied, including transformations, paraphrasing (back-translation), and text generation using generative adversarial networks, to small datasets such as song lyrics, social media comments, headlines from national newspapers in Chile, and survey responses from higher education students. The findings show that the Convolutional Neural Network (CNN) classifier achieved the most significant improvement, with an 18% increase using the Generative Adversarial Networks for Sentiment Text (SentiGan) on the Aggressiveness (Seriousness) dataset. Additionally, the same classifier model showed an 11% improvement using the Easy Data Augmentation (EDA) on the Gender-Based Violence dataset. The performance of the Bidirectional Encoder Representations from Transformers (BETO) also improved by 10% on the back-translation augmented version of the October 18 dataset, and by 4% on the EDA augmented version of the Teaching survey dataset. These results suggest that data augmentation techniques enhance performance by transforming text and adapting it to the specific characteristics of the dataset. Through experimentation with various augmentation techniques, this research provides valuable insights into the analysis of subjectivity in Spanish texts and offers guidance for selecting algorithms and techniques based on dataset features.

If you are in need of emergency shelter space, please call the City of Toronto’s Central Intake line at 416-338-4766 or 1-877-338-3398. This catalogue entry provides two data sets related to calls to Central Intake. Central Intake is a City-operated, 24/7 telephone-based service that offers referrals to emergency shelter and other overnight accommodation, as well as information about other homelessness services. These two data sets provide information about calls received by Central Intake, the outcomes of those calls, and the number of individuals who could not be matched to a shelter space each day. The first, Central Intake Service Queue Data, provides counts of the number of unique individuals who contacted Central Intake to access emergency shelter but were not matched to a shelter space. Generated through Central Intake caseworkers' use of the City's Shelter Management Information System (SMIS), the data are reported as a count for every operational day. The SMIS service queue for Central Intake records when a bed is requested for a caller seeking a shelter space. Those callers who could not be matched to an available space that suits their needs at the time of their call remain in the queue until they can be provided a referral or until the closeout process at the end of the night (i.e. 4:00 a.m.). Service Queue data combines data exported from the Central Intake service queue at 4:00 a.m., with manually coded outcome data based on the review of each individual's SMIS records for the day. SSHA began collecting data on how many people remain unmatched in the service queue over a 24 hour period at the beginning of November 2020. Given the manual nature of the preparation of the data in this data set, this file will be updated on a monthly basis. Data will be reported separately for every operational day in that month. The second data set, Central Intake Call Wrap-Up Codes Data, provides counts of calls answered by Central Intake, classified by the nature of the call. When a call is handled by a caseworker at Central Intake, the caseworker assigns a wrap-up code to the call. This tracking allows for analysis of call trends. Central Intake uses 13 distinct wrap-up codes to code the calls they receive. This data set provides a daily summary of the number of calls received by each call wrap-up code. The data are manually retrieved from the City's call centre database reports. Given the manual nature of the preparation of the data in this data set, this file will be updated on a monthly basis. Data will be reported separately for every operational day in that month. Please note that while the wrap-up codes provide information related to the volume and type of calls answered by Central Intake, the data do not track requests made by unique individuals nor the ultimate outcomes of referrals. Please also note that the previews and Data Features below only show information pertaining to the Central Intake Call Wrap-Up Codes Data dataset.

On Earth, people spend 90% of their time indoors where dust and moisture can facilitate rapid microbial growth, especially fungi. The International Space Station is a specialized closed environment that contains own unique indoor microbiome. Elevated moisture such as from a temporary ventilation system malfunction may lead to unintended microbial growth indoors, which is associated with negative health outcomes and degradation of essential built environment materials. We need to develop a predictive approach for modeling microbial growth to understand when it may occur in these critical indoor spaces. Here we demonstrate that exposure to even fluctuating elevated relative humidity above 80% can lead to rapid microbial growth and community composition changes in dust from spacecraft. We were able to model fungal growth in space station dust using the time-of-wetness framework with activation and deactivation limited growth occurring at 85% and 100% relative humidity conditions, respectively. Alpha and beta diversity of fungi was altered with both significantly decreasing as relative humidity and time elevated increased. Our results demonstrate that we can use moisture conditions to develop predictive models for fungal growth and composition. Understanding microbial growth in spacecraft can protect astronaut health, spacecraft integrity, and promote planetary protection as human activity increases in low-Earth orbit, the moon, Mars, and beyond.

People Counts This data set is sourced from Dundee City Council’s Public Space Camera Surveillance System. It shows a count of people in 8 specified areas across Dundee. The data set shows a snapshot of people within these areas every Monday, Wednesday and Saturday during the period 1pm-2pm.This data is experimental and subject to further refinement. Please note that due the nature of CCTV cameras at times data may not be collected as specified above. Therefore, caution should be exercised when analysing data and drawing conclusions for this data set.CCTV datasets contain information on object detections taken from a selection of the CCTV cameras throughout Dundee City. CCTV images are translated into object counts, objects counted include ‘person’, ‘car’, ‘bicycle’, ‘bus’, ‘motorcycle', 'truck, ‘pickup truck 'and ‘van’. The data is generated and owned by Dundee City Council. Copyright © Dundee City Council 2022. This dataset is available for use under the Open Government Licence.Background information about the Dundee CCTV cameras including a map showing the location of the cameras is available on the Dundee City Council website and can be accessed using the following link:https://www.dundeecity.gov.uk/service-area/city-development/sustainable-transport-and-roads/dundees-public-space-camera-surveillance-system

Introduction

The GiGL Spaces to Visit dataset provides locations and boundaries for open space sites in Greater London that are available to the public as destinations for leisure, activities and community engagement. It includes green corridors that provide opportunities for walking and cycling.

The dataset has been created by Greenspace Information for Greater London CIC (GiGL). As London’s Environmental Records Centre, GiGL mobilises, curates and shares data that underpin our knowledge of London’s natural environment. We provide impartial evidence to support informed discussion and decision making in policy and practice.

GiGL maps under licence from the Greater London Authority.

Description

This dataset is a sub-set of the GiGL Open Space dataset, the most comprehensive dataset available of open spaces in London. Sites are selected for inclusion in Spaces to Visit based on their public accessibility and likelihood that people would be interested in visiting.

The dataset is a mapped Geographic Information System (GIS) polygon dataset where one polygon (or multi-polygon) represents one space. As well as site boundaries, the dataset includes information about a site’s name, size and type (e.g. park, playing field etc.).

GiGL developed the Spaces to Visit dataset to support anyone who is interested in London’s open spaces - including community groups, web and app developers, policy makers and researchers - with an open licence data source. More detailed and extensive data are available under GiGL data use licences for GIGL partners, researchers and students. Information services are also available for ecological consultants, biological recorders and community volunteers – please see www.gigl.org.uk for more information.

Please note that access and opening times are subject to change (particularly at the current time) so if you are planning to visit a site check on the local authority or site website that it is open.

The dataset is updated on a quarterly basis. If you have questions about this dataset please contact GiGL’s GIS and Data Officer.

Data sources

The boundaries and information in this dataset, are a combination of data collected during the London Survey Method habitat and open space survey programme (1986 – 2008) and information provided to GiGL from other sources since. These sources include London borough surveys, land use datasets, volunteer surveys, feedback from the public, park friends’ groups, and updates made as part of GiGL’s on-going data validation and verification process.

Due to data availability, some areas are more up-to-date than others. We are continually working on updating and improving this dataset. If you have any additional information or corrections for sites included in the Spaces to Visit dataset please contact GiGL’s GIS and Data Officer.

NOTE: The dataset contains OS data © Crown copyright and database rights 2025. The site boundaries are based on Ordnance Survey mapping, and the data are published under Ordnance Survey's 'presumption to publish'. When using these data please acknowledge GiGL and Ordnance Survey as the source of the information using the following citation:

‘Dataset created by Greenspace Information for Greater London CIC (GiGL), 2025 – Contains Ordnance Survey and public sector information licensed under the Open Government Licence v3.0 ’

This dataset contains unreleased data from the BepiColombo mission for the periods 2021-12-06 to 2021-12-07 and 2021-12-24 to 2021-12-26 during the cruise phase to Mercury. In particular, it contains magnetic field observations from the MPO-MAG instrument (Heyner et al. 2021).

****************IMPORTANT NOTE****************

These data have not been released yet to the Planetary Science Archive (PSA). As consequence, they may still suffer some changes due to future calibrations before they are released to the PSA.

Therefore, we strongly encourage all the users to contact the following people before using this dataset.

• Dr Beatriz Sánchez-Cano, University of Leicester, BepiColombo Guest Investigator, bscmdr1@leicester.ac.uk

• Dr Daniel Heyner, TU Braunschweig, Principal Investigator of MPO-MAG, d.heyner@tu-bs.de

File description:

It contains derived data from the outbound sensor. These files have 5 columns with the following information:

1 -Data time 2 - Total magnetic field 3 - B_r component of the Magnetic Field 4 - B_t component of the Magnetic Field 5 - B_n component of the Magnetic Field

Reference:

D. Heyner et al.,(2021), The BepiColombo Planetary Magnetometer MPO-MAG: What can we Learn From the Hermean Magnetic Field? Space Sci Rev, 217, 52. https://doi.org/10.1007/s11214-021-00822-x

Introduction The GiGL Open Space Friends Group subset provides locations and boundaries for selected open space sites in Greater London. The chosen sites represent sites that have established Friends Groups in Greater London and are therefore important to local communities, even if they may not be accessible open spaces, or don’t typically function as destinations for leisure, activities and community engagement*. Friends Groups are groups of interested local people who come together to protect, enhance and improve their local open space or spaces. The dataset has been created by Greenspace Information for Greater London CIC (GiGL). As London’s Environmental Records Centre, GiGL mobilises, curates and shares data that underpin our knowledge of London’s natural environment. We provide impartial evidence to support informed discussion and decision making in policy and practice. GiGL maps under licence from the Greater London Authority. *Publicly accessible sites for leisure, activities and community engagement can be found in GiGL's Spaces to Visit dataset Description This dataset is a sub-set of the GiGL Open Space dataset, the most comprehensive dataset available of open spaces in London. Sites are selected for inclusion in the Friends Group subset based on whether there is a friends group recorded for the site in the Open Space dataset. The dataset is a mapped Geographic Information System (GIS) polygon dataset where one polygon (or multi-polygon) represents one space. As well as site boundaries, the dataset includes information about a site’s name, size, access and type (e.g. park, playing field etc.) and the name and/or web address of the site’s friends group. GiGL developed the dataset to support anyone who is interested in identifying sites in London with friends groups - including friends groups and other community groups, web and app developers, policy makers and researchers - with an open licence data source. More detailed and extensive data are available under GiGL data use licences for GIGL partners, researchers and students. Information services are also available for ecological consultants, biological recorders, community groups and members of the public – please see www.gigl.org.uk for more information. The dataset is updated on a quarterly basis. If you have questions about this dataset please contact GiGL’s GIS and Data Officer. Data sources The boundaries and information in this dataset are a combination of data collected during the London Survey Method habitat and open space survey programme (1986 – 2008) and information provided to GiGL from other sources since. These sources include London borough surveys, land use datasets, volunteer surveys, feedback from the public, park friends’ groups, and updates made as part of GiGL’s on-going data validation and verification process. This is a preliminary version of the dataset as there is currently low coverage of friends groups in GiGL’s Open Space database. We are continually working on updating and improving this dataset. If you have any additional information or corrections for sites included in GiGL’s Friends Group subset please contact GiGL’s GIS and Data Officer. NOTE: The dataset contains OS data © Crown copyright and database rights 2024. The site boundaries are based on Ordnance Survey mapping, and the data are published under Ordnance Survey's 'presumption to publish'. When using these data please acknowledge GiGL and Ordnance Survey as the source of the information using the following citation: ‘Dataset created by Greenspace Information for Greater London CIC (GiGL), 2024 – Contains Ordnance Survey and public sector information licensed under the Open Government Licence v3.0 ’

Here are a few use cases for this project:

1. Smart Surveillance: The "Custom ir-dataset" could be used in an advanced surveillance system where it identifies people in a strategic locations like supermarkets, airports, and other public places, thereby contributing to public safety.

2. Social Distancing Monitoring: In the Covid-19 era, this dataset could be used to develop a model to enforce social distancing rules by identifying the number of people and their distance from each other in a given space.

3. Occupancy Sensing: The model could be applied in smart building technology where it determines the number of occupants in a building, allowing for efficient energy use based on real-time occupancy data.

4. Interactive Advertising: Businesses could use it to understand consumer demographics and behaviors in physical stores by identifying customers, tracking their movements, and analyzing their interactions with different products.

5. Event Management: For managers of large events like concerts or conferences, using a model built on this dataset could facilitate headcount, crowd control and emergency responses by identifying people and their distribution in the venue.