Many existing complex space systems have a significant amount of historical maintenance and problem data bases that are stored in unstructured text forms. The problem that we address in this paper is the discovery of recurring anomalies and relationships between problem reports that may indicate larger systemic problems. We will illustrate our techniques on data from discrepancy reports regarding software anomalies in the Space Shuttle. These free text reports are written by a number of different people, thus the emphasis and wording vary considerably. With Mehran Sahami from Stanford University, I'm putting together a book on text mining called "Text Mining: Theory and Applications" to be published by Taylor and Francis.

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.

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 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 ’

🇬🇧 United Kingdom English 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 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 ’

🇬🇧 United Kingdom English 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 ’

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

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.

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.

Thursday Early Evening City Centre - Counts of People This data set is sourced from Dundee City Council’s Public Space Camera Surveillance System. It shows a count of people in 4 specified areas within the City Centre namely:• 316 – High St / City Sq• 421 – Perth Rd/ South Tay St• 323 – Union St/ Shore Tce• 315 – Commercial St / High StThe data set shows a count of people within these areas every Thursday evening during the period 5pm – 7pm. 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

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.

Data Download: The Secured Areas 2024 dataset is also available as an ESRI polygon geodatabase dataset.The secured areas dataset shows public and private lands that are permanently secured against conversion to development, GAP 1-3, through fee ownership, easements, or permanent conservation restrictions. It also includes a set of more temporary easement and GAP 4 open space lands not permanently secured for nature conservation. TNC compiled these data from state, federal, and private sources and assigned a GAP Status and other standard attribute fields to the best of our ability. The Secured Areas dataset is a TNC product created primarily for estimating current extent and status of secured lands with a conservation focus, GAP 1-3. The non GAP 1-3 lands are less comprehensively mapped given the lack of their inclusion in some primary source datasets, but they are included as available in our source datasets. Any updates, corrections, or discrepancies with respect to official versions of source federal, state, or local protected areas databases should be viewed as provisional until such time as such changes have been reviewed and accepted by the official data stewards for those other protected areas databases.GAP STATUS GAP status is a classification developed by the US Fish and Wildlife Service, to reflect the intent of the landowner or easement holder. GAP 1 and 2 are commonly thought of as “protected” for nature", while GAP 3 are “multiple-use” lands. Other temporary conservation easement lands and/or protected open space without a conservation value or intent are assigned GAP 4. (Citation: Crist, P.J., B. Thompson, T. C. Edwards, C. G. Homer, S. D. Bassett. 1998. Mapping and Categorizing Land Stewardship. A Handbook for Conducting Gap Analysis.) In addition to GAP 1-3 lands, in our TNC secured areas product we classified six additional classes of open space lands (permanent agricultural easements, temporary conservation easements, temporary agricultural easements, urban parks, state board lands, other GAP 4 lands). The following definitions guided our assignment of lands into the following nine classes:TNC CLASS CODE (fields TNCCLASS, TNCCLASS_D)1 = GAP 1: Permanently Secured for Nature and Natural Processes. Managed for biodiversity with all natural processes, little to no human intervention. Primary intention of the owner or easement holder is for biodiversity, nature protection, natural diversity, and natural processes. Land and water managed through natural processes including disturbances with little or no human intervention.Examples: wilderness area, some national parks2 = GAP 2 = Permanently Secured for Nature with Management: Managed for biodiversity, with hands on management or interventions. Primary intention of the owner or easement holder is for biodiversity conservation, nature protection, and natural diversity. Land and water managed for natural biodiversity conservation, but may include some hands on manipulation or suppression of disturbance and natural processes. Examples: national wildlife refuges, areas of critical environmental concern, inventoried roadless areas, some natural areas and preserves3 = GAP 3: Permanently Secured for Multiple Uses, including nature: Primary intention of the owner or easement holder for multiple uses. Strong focus on recreational use, game species production, timber production, grazing and other uses in additional to these lands providing some biodiversity value. May include extractive uses of a broad, low-intensity type (e.g. some logging. grazing) or of a localized intense type (e.g. mining, military artillery testing area, public access beach area within large natural state park). Examples: recreation focused protected areas such as state parks, state recreation areas, wildlife management areas, gamelands, state and national forests, local conservation lands with primary focus on recreational use.38 = State Board Lands and State Trust Lands: Lands in western and some southern states that are owned by the state and prevented from being developed, but which are managed to produce long term sustained revenue for the state’s educational system. These lands were separated from other protected multiple use lands in GAP 3. Most of these lands are subject to timber extraction and management for profitable forest product production. Some also have agricultural use and revenue generated from grazing and/or agricultural production leasing. These lands are not specifically managed for biodiversity values, and some are occasionally sold in periodic auctions by the state for revenue generation. Note this type of land is most commonly assigned GAP 3 in the PAD-US GAP classification.39 = Permanent Agricultural Easements: Conservation land where the primary intent is the preservation of farmland. Land is in a permanent agricultural easement or an easement to maintain grass cover. The land will not be converted to a built or paved development. Example: vegetable farm with permanent easement to prevent development. Note this type of land would be assigned GAP 4 in the PAD-US GAP classification.4 = GAP 4: Areas with no known mandate for permanent biodiversity protection. Municipal lands and other protected open space (e.g. town commons, historic parks) where the intention in management and the use of the open space is not for permanent biodiversity values. It was beyond our capacity to comprehensively compile these GAP 4 lands, and as such, they are included only where source data made it feasible to easily incorporate them. 5 = Temporary Natural Easements: Note this type of land would be assigned GAP 4 in the PAD-US GAP classification.6 = Temporary Agricultural Easements: Note this type of land would be assigned GAP 4 in the PAD-US GAP classification.9 = Urban Parks: While unlikely to have biodiversity value, urban parks provide important places for recreation and open space for people. We went through and identified parks whose name is recreation based (i.e. Playground, Community garden, Golf, fields, baseball, soccer, Mini, school, elementary, Triangle, Pool, Aquatic, Sports, Pool, Athletic, Pocket, Splash, Skate, Dog, Cemetery, Boat). Note this type of land would be assigned GAP 4 in the PAD-US GAP classification.OWNERSHIP DEFINITIONSThe type of owner or interest holder for each polygon was assigned to a set of simple reporting categories as follows (see fields = Fee_Own_T and InterstH_T )TVA -Tennessee Valley Authority, BLM -Bureau of Land Management, , BOR- Bureau of Reclamation, FWS - U.S. Fish & Wildlife Service, UFS - Forest Service, DOD - Department of Defense, ACE - Army Corps of Engineers , DOE - Department of Energy, NPS - National Park Service, NRC - Natural Resources Conservation Service, FED – Federal Other, TRB - American Indian Lands, SPR - State Park and Recreation , SDC - State Department of Conservation, SLB - State Land Board , SFW - State Fish and Wildlife, SNR - State Department of Natural Resources, STL -State Department of Land, STA - Other or Unknown State Land, REG - Regional Agency Land, LOC – Local Government (City, County), NGO - Non-Governmental Organization, PVT- Private, JNT - Joint , OTH- Other , UNK - UnknownPROTECTION TYPE DEFINITIONS: (see field PRO_TYPE_D)DesignationEasementEasement and DesignationFeeFee and DesignationFee and EasementFee, Easement, and DesignationDATA SOURCES: The 2024 CONUS Secured Areas dataset was compiled by TNC from multiple sources. These include state, federal, and other non-profit and land trust data. The primarily datasets are listed below. 1. U.S. Geological Survey (USGS) Gap Analysis Project (GAP), 2022. Protected Areas Database of the United States (PAD-US) 3.0: U.S. Geological Survey data release, https://doi.org/10.5066/P9Q9LQ4B.) Downloaded 1/10/2024 Note this dataset was used as the primary source outside of the Northeast 13 states. For the Northeast states, please see more detailed source information below.2. National Conservation Easement Database (NCED) https://www.conservationeasement.us/ Downloaded 1/12/2024. Note this dataset was used outside the Northeast 13 states. For Northeast states, please see more detailed source information below. 3. Natural Resources Conservation Service (NRCS) Easements. 2024. Downloaded 1/12/2024 https://datagateway.nrcs.usda.gov/4. Conservation Science Partners, Inc. 2024. Wild and Scenic River corridor areas. Dataset compiled by Conservation Science Partners, Inc. for American Rivers as of 2/14/2024 (per. Communication Lise Comte , Conservation Science Partners, Inc. 2/14/2024)5. The Nature Conservancy. 2024. TNC Lands. Downloaded 3/1/2024.6. The Nature Conservancy Center for Resilient Conservation Science. 2021. Military Lands of the Southeast United States. Extracted from Secured areas spatial database (CONUS) 2021. https://tnc.maps.arcgis.com/home/item.html?id=e033e6bf6069459592903a04797b8b07.7. The Nature Conservancy Center for Resilient Conservation Science. 2022. Northeast States Secured Areas. https://tnc.maps.arcgis.com/home/item.html?id=fb80d71d5aa74a91a25e55b6f1810574

This study examines the spatial patterns of homelessness and resources for the homeless population in Louisville, KY with the goal of identifying where homeless populations are located in relation to resources. Working with census data and some of the resources for the homeless, this study uncovers the realities that the homeless face in different parts of the city. This research research was made as a senior thesis for the University of Louisville's department of Geographic and Environmental Sciences. Table 1. Income and Poverty between the United States and Louisville/Jefferson County metro government, Kentucky in 2019 (United States Census Bureau 2021)Homeless people are thought of as less than full citizens. Whether the rest of the city's people agree or disagree, they are citizens, and should have rights to the city as much as everyone else. The opioid crisis, unmanaged mental illnesses, lack of employment, and other issues like limitations on affordable housing have increased the population of homeless people in Louisville in recent years (Reed 2021). More than 1.5 million children experience homelessness in the United States (Poverty USA 2019). The poverty rate in Louisville, Kentucky is 15.9%, and 1 in 10 renters were facing eviction as of 2019. The 2019 Point In Time Count shows that on a randomly picked night in Louisville, 1071 of the city's people are experiencing homelessness, which is an increase of 15% from the 2018 count (Coalition for the Homeless 2019). The previous data compared to the count for 2020 of 1102 people, shows a trend in increasing homeless population (Coalition for the Homeless 2020).

Context

Similar to others who have created HR data sets, we felt that the lack of data out there for HR was limiting. It is very hard for someone to test new systems or learn People Analytics in the HR space. The only dataset most HR practitioners have is their real employee data and there are a lot of reasons why you would not want to use that when experimenting. We hope that by providing this dataset with an evergrowing variation of data points, others can learn and grow their HR data analytics and systems knowledge.

Some example test cases where someone might use this dataset:

HR Technology Testing and Mock-Ups Engagement survey tools HCM tools BI Tools Learning To Code For People Analytics Python/R/SQL HR Tech and People Analytics Educational Courses/Tools

Content

The core data CompanyData.txt has the basic demographic data about a worker. We treat this as the core data that you can join future data sets to.

Please read the Readme.md for additional information about this along with the Changelog for additional updates as they are made.

Acknowledgements

Initial names, addresses, and ages were generated using FakenameGenerator.com. All additional details including Job, compensation, and additional data sets were created by the Koluit team using random generation in Excel.

Inspiration

Our hope is this data is used in the HR or Research space to experiment and learn using HR data. Some examples that we hope this data will be used are listed above.

Contact Us

Have any suggestions for additions to the data? See any issues with our data? Want to use it for your project? Please reach out to us! https://koluit.com/ ryan@koluit.com

Few human endeavors present as much of a planning and scheduling challenge as space flight, particularly manned space flight. Just on the operational side of it, efforts of thousands of people across hundreds of organizations need to be coordinated. Numerous tasks of varying complexity and nature, from scientific to construction, need to be accomplished within limited mission time frames. Resources need to be carefully managed and contingencies worked out, often on a very short notice. From the beginning of the NASA space program, planning has been done by large teams of domain experts working months, sometimes years, to put together a single mission. This approach, while proven very reliable up to now, is becoming increasingly harder to sustain. Elevated levels of NASA space activities, from deployment of the new Crew Exploration Vehicle (CEV) and completion of the International Space Station (ISS), to the planned lunar missions and permanent lunar bases, will put an even greater strain on this largely manual process. While several attempts to automate it have been made in the past, none have fully succeeded. In this paper we describe the current NASA planning methods, outline their advantages and disadvantages, discuss the planning challenges of upcoming missions and propose a distributed planning/scheduling framework (CMMD) aimed at unifying and optimizing the planning effort. CMMD will not attempt to make the process completely automated, but rather serve in a decision support capacity for human managers and planners. It will help manage information gathering, creation of partial and consolidated schedules, inter-team negotiations, contingencies investigation, and rapid re-planning when the situation demands it. The first area of CMMD application will be planning for Extravehicular Activities (EVA) and associated logistics. Other potential applications, not only in the space flight domain, and future research efforts will be discussed as well.

Accident-Detection-Model

Accident Detection Model is made using YOLOv8, Google Collab, Python, Roboflow, Deep Learning, OpenCV, Machine Learning, Artificial Intelligence. It can detect an accident on any accident by live camera, image or video provided. This model is trained on a dataset of 3200+ images, These images were annotated on roboflow.

Problem Statement

• Road accidents are a major problem in India, with thousands of people losing their lives and many more suffering serious injuries every year.
• According to the Ministry of Road Transport and Highways, India witnessed around 4.5 lakh road accidents in 2019, which resulted in the deaths of more than 1.5 lakh people.
• The age range that is most severely hit by road accidents is 18 to 45 years old, which accounts for almost 67 percent of all accidental deaths.

Accidents survey

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Literature Survey

• Sreyan Ghosh in Mar-2019, The goal is to develop a system using deep learning convolutional neural network that has been trained to identify video frames as accident or non-accident.
• Deeksha Gour Sep-2019, uses computer vision technology, neural networks, deep learning, and various approaches and algorithms to detect objects.

Research Gap

• Lack of real-world data - We trained model for more then 3200 images.
• Large interpretability time and space needed - Using google collab to reduce interpretability time and space required.
• Outdated Versions of previous works - We aer using Latest version of Yolo v8.

Proposed methodology

• We are using Yolov8 to train our custom dataset which has been 3200+ images, collected from different platforms.
• This model after training with 25 iterations and is ready to detect an accident with a significant probability.

Model Set-up

Preparing Custom dataset

• We have collected 1200+ images from different sources like YouTube, Google images, Kaggle.com etc.
• Then we annotated all of them individually on a tool called roboflow.
• During Annotation we marked the images with no accident as NULL and we drew a box on the site of accident on the images having an accident
• Then we divided the data set into train, val, test in the ratio of 8:1:1
• At the final step we downloaded the dataset in yolov8 format.
  #### Using Google Collab
• We are using google colaboratory to code this model because google collab uses gpu which is faster than local environments.
• You can use Jupyter notebooks, which let you blend code, text, and visualisations in a single document, to write and run Python code using Google Colab.
• Users can run individual code cells in Jupyter Notebooks and quickly view the results, which is helpful for experimenting and debugging. Additionally, they enable the development of visualisations that make use of well-known frameworks like Matplotlib, Seaborn, and Plotly.
• In Google collab, First of all we Changed runtime from TPU to GPU.
• We cross checked it by running command ‘!nvidia-smi’
  #### Coding
• First of all, We installed Yolov8 by the command ‘!pip install ultralytics==8.0.20’
• Further we checked about Yolov8 by the command ‘from ultralytics import YOLO from IPython.display import display, Image’
• Then we connected and mounted our google drive account by the code ‘from google.colab import drive drive.mount('/content/drive')’
• Then we ran our main command to run the training process ‘%cd /content/drive/MyDrive/Accident Detection model !yolo task=detect mode=train model=yolov8s.pt data= data.yaml epochs=1 imgsz=640 plots=True’
• After the training we ran command to test and validate our model ‘!yolo task=detect mode=val model=runs/detect/train/weights/best.pt data=data.yaml’ ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt conf=0.25 source=data/test/images’
• Further to get result from any video or image we ran this command ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt source="/content/drive/MyDrive/Accident-Detection-model/data/testing1.jpg/mp4"’
• The results are stored in the runs/detect/predict folder.
  Hence our model is trained, validated and tested to be able to detect accidents on any video or image.

Challenges I ran into

I majorly ran into 3 problems while making this model

• I got difficulty while saving the results in a folder, as yolov8 is latest version so it is still underdevelopment. so i then read some blogs, referred to stackoverflow then i got to know that we need to writ an extra command in new v8 that ''save=true'' This made me save my results in a folder.
• I was facing problem on cvat website because i was not sure what

The Movie Review Data is a collection of movie reviews retrieved from the imdb.com website in the early 2000s by Bo Pang and Lillian Lee. The reviews were collected and made available as part of their research on natural language processing. The reviews were originally released in 2002, but an updated and cleaned up version was released in 2004, referred to as v2.0. The dataset is comprised of 1,000 positive and 1,000 negative movie reviews drawn from an archive of the rec.arts.movies.reviews newsgroup hosted at IMDB. The authors refer to this dataset as the polarity dataset.

Our data contains 1000 positive and 1000 negative reviews all written before 2002, with a cap of 20 reviews per author (312 authors total) per category. We refer to this corpus as the polarity dataset. - A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts, 2004.

The data has been cleaned up somewhat, for example: - The dataset is comprised of only English reviews. - All text has been converted to lowercase. - There is white space around punctuation like periods, commas, and brackets. - Text has been split into one sentence per line.

The data has been used for a few related natural language processing tasks. For classification, the performance of classical models (such as Support Vector Machines) on the data is in the range of high 70% to low 80% (e.g. 78%-to-82%). More sophisticated data preparation may see results as high as 86% with 10-fold cross-validation. This gives us a ballpark of low-to-mid 80s if we were looking to use this dataset in experiments on modern methods.

... depending on choice of downstream polarity classifier, we can achieve highly statistically significant improvement (from 82.8% to 86.4%) - A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts, 2004.

After unzipping the file, you will have a directory called txt sentoken with two sub- directories containing the text neg and pos for negative and positive reviews. Reviews are stored one per file with a naming convention from cv000 to cv999 for each of neg and pos. Next, let’s look at loading the text data.

Dataset

This dataset was created by AlexAgMart

Contents

NASA Tweets

Analyzing Engagement Patterns

By Twitter [source]

About this dataset

This dataset provides a comprehensive look at how one of the world’s most influential space exploration agencies utilizes Twitter to communicate and interact with their followers. With over 20 million NASA followers, the organization has quickly become trustworthy in providing the public with accurate information about space exploration, satellite data, and educational opportunities. This dataset includes a wide variety of metrics to understand media usage for each tweet, reactions from users regarding NASA's tweets via likes and retweets, as well as providing insight into any outlinks associated with each tweet. It features more than 4000 individual tweets from NASA from 2015-2017 that have been carefully selected for analysis. Captured within this study are pertinent attributes related to how humans interact – both emotionally and cognitively – with technology in an increasingly digital age

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Welcome to the dataset 'NASA Tweets: Likelihood, Media Usage, and Outlink Engagement'! This dataset contains over 3 million tweets from the official Twitter account of NASA. With this data, you can examine a variety of information to better understand how people interact with and engage with messages from NASA through social media platforms.
Using this dataset, you can analyze data related to tweets including the likelihood of being retweeted or favorited along with featured media usage and outlink engagements associated with each tweet. Additionally, there is summary data available for each tweet related to when it was posted (month/year), how many users were tagged in the tweet, its sentiment expression type, sentiment score and magnitude score among other variables.

Research Ideas

• Identifying key influencers and the topics they are most passionate about in order to create tailored messaging campaigns.
• Analyzing the structure, reach, effectiveness, and sentiment of certain keywords or hashtags to develop a more effective social media strategy.
• Exploring campaigns that have been particularly successful by analyzing the rate at which people are engaging with content and outlinks associated with NASA tweets

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Twitter.

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Context

• The metaverse, a living and breathing space that blends physical and digital, is quickly evolving from a science fiction dream into a reality with endless possibilities. A world where people can interact virtually, create and exchange digital assets for real-world value, own digital land, engage with digitized real-world products and services, and much more.

• Major tech giants are beginning to recognize the viability and potential of metaverses, following Facebook’s groundbreaking Meta rebrand announcement. In addition to tech companies, entertainment brands like Disney have also announced plans to take the leap into virtual reality.

• While the media hype is deafening, your average netizen isn’t fully aware of what a metaverse is, how it operates and, most importantly—what benefits and opportunities it can offer them as a user.

https://cdn.images.express.co.uk/img/dynamic/22/590x/Metaverse-tokens-cryptocurrency-explained-ethereum-killers-new-coins-digital-currency-meta-news-1518777.jpg?r=1638256864800" alt="">

What Is The Metaverse?

• In its digital iteration, a metaverse is a virtual world based on blockchain technology. This all-encompassing space allows users to work and play in a virtual reflection of real-life and fantasy scenarios, an online reality, ranging from sci-fi and dragons to more practical and familiar settings like shopping centers, offices, and even homes.

• Users can access metaverses via computer, handheld device, or complete immersion with a VR headset. Those entering the metaverse get to experience living in a digital realm, where they will be able to work, play, shop, exercise, and socialize. Users will be able to create their own avatars based on face recognition, set up their own businesses of any kind, buy real estate, create in-world content and asset,s and attend concerts from real-world superstars—all in one virtual environment,

• With that said, a metaverse is a virtual world with a virtual economy. In most cases, it is an online reality powered by decentralized finance (DeFi), where users exchange value and assets via cryptocurrencies and Non-Fungible Tokens.

What Are Metaverse Tokens?

• Metaverse tokens are a unit of virtual currency used to make digital transactions within the metaverse. Since metaverses are built on the blockchain, transactions on underlying networks are near-instant. Blockchains are designed to ensure trust and security, making the metaverse the perfect environment for an economy free of corruption and financial fraud.

• Holders of metaverse tokens can access multiple services and applications inside the virtual space. Some tokens give special in-game abilities. Other tokens represent unique items, like clothing for virtual avatars or membership for a community. If you’ve played MMO games like World of Warcraft, the concept of in-game items and currencies are very familiar. However, unlike your traditional virtual world games, metaverse tokens have value inside and outside the virtual worlds. Metaverse tokens in the form of cryptocurrency can be exchanged for fiat currencies. Or if they’re an NFT, they can be used to authenticate ownership to tethered real-world assets like collectibles, works or art, or even cups of coffee.

• Some examples of metaverse tokens include SAND of the immensely popular Sandbox metaverse. In The Sandbox, users can create a virtual world driven by NFTs. Another token is MANA of the Decentraland project, where users can use MANA to purchase plots of digital real estate called “LAND”. It is even possible to monetize the plots of LAND purchased by renting them to other users for fixed fees. The ENJ token of the Enjin metaverse is the native asset of an ecosystem with the world’s largest game/app NFT networks.

Dataset Information

• The dataset brings 198 metaverse cryptos. Pls refer to the file Metaverse coins.csv to find the list of metaverse crypto coins.

• The dataset will be updated on a weekly basis with more and more additional metaverse tokens, Stay tuned ⏳

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.