The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

Colleges and UniversitiesThis feature layer, utilizing data from the National Center for Education Statistics (NCES), displays colleges and universities in the U.S. and its territories. NCES uses the Integrated Postsecondary Education Data System (IPEDS) as the "primary source for information on U.S. colleges, universities, and technical and vocational institutions." According to NCES, this layer "contains directory information for every institution in the 2021-22 IPEDS universe. Includes name, address, city, state, zip code and various URL links to the institution's home page, admissions, financial aid offices and the net price calculator. Identifies institutions as currently active, institutions that participate in Title IV federal financial aid programs for which IPEDS is mandatory. It also includes variables derived from the 2021-22 Institutional Characteristics survey, such as control and level of institution, highest level and highest degree offered and Carnegie classifications."Gallaudet UniversityData currency: 2021Data source: IPEDS Complete Data FilesData modification: Removed fields with coded values and replaced with descriptionsFor more information: Integrated Postsecondary Education Data SystemSupport documentation: IPEDS Complete Data Files > Directory Information > DictionaryFor feedback, please contact: ArcGIScomNationalMaps@esri.comU.S. Department of Education (ED)Per ED, "ED's mission is to promote student achievement and preparation for global competitiveness by fostering educational excellence and ensuring equal access.ED was created in 1980 by combining offices from several federal agencies." ED's employees and budget "are dedicated to:Establishing policies on federal financial aid for education, and distributing as well as monitoring those funds.Collecting data on America's schools and disseminating research.Focusing national attention on key educational issues.Prohibiting discrimination and ensuring equal access to education."

Please cite the following paper when using this dataset: N. Thakur, “A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave,” Journal of Data, vol. 7, no. 8, p. 109, Aug. 2022, doi: 10.3390/data7080109 Abstract The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations, centered around information seeking and sharing, related to online learning. Mining such conversations, such as Tweets, to develop a dataset can serve as a data resource for interdisciplinary research related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore this work presents a large-scale public Twitter dataset of conversations about online learning since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset files contain the raw version that comprises 52,868 Tweet IDs (that correspond to the same number of Tweets) and the cleaned and preprocessed version that contains 46,208 unique Tweet IDs. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter and the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management. Data Description The dataset comprises 7 .txt files. The raw version of this dataset comprises 6 .txt files (TweetIDs_Corona Virus.txt, TweetIDs_Corona.txt, TweetIDs_Coronavirus.txt, TweetIDs_Covid.txt, TweetIDs_Omicron.txt, and TweetIDs_SARS CoV2.txt) that contain Tweet IDs grouped together based on certain synonyms or terms that were used to refer to online learning and the Omicron variant of COVID-19 in the respective tweets. The cleaned and preprocessed version of this dataset is provided in the .txt file - TweetIDs_Duplicates_Removed.txt. The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (link to download the application: https://github.com/DocNow/hydrator/releases and link to a step-by-step tutorial: https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweetsr) may be used. The list of all the synonyms or terms that were used for the dataset development is as follows: COVID-19: Omicron, COVID, COVID19, coronavirus, coronaviruspandemic, COVID-19, corona, coronaoutbreak, omicron variant, SARS CoV-2, corona virus online learning: online education, online learning, remote education, remote learning, e-learning, elearning, distance learning, distance education, virtual learning, virtual education, online teaching, remote teaching, virtual teaching, online class, online classes, remote class, remote classes, distance class, distance classes, virtual class, virtual classes, online course, online courses, remote course, remote courses, distance course, distance courses, virtual course, virtual courses, online school, virtual school, remote school, online college, online university, virtual college, virtual university, remote college, remote university, online lecture, virtual lecture, remote lecture, online lectures, virtual lectures, remote lectures A description of the dataset files is provided below: TweetIDs_Corona Virus.txt – Contains 321 Tweet IDs correspond to tweets that comprise the keywords – "corona virus" and one or more keywords/terms that refer to online learning TweetIDs_Corona.txt – Contains 1819 Tweet IDs correspond to tweets that comprise the keyword – "corona" or "coronaoutbreak" and one or more keywords/terms that refer to online learning TweetIDs_Coronavirus.txt – Contains 1429 Tweet IDs correspond to tweets that comprise the keywords – "coronavirus" or "coronaviruspandemic" and one or more keywords/terms that refer to online learning TweetIDs_Covid.txt – Contains 41088 Tweet IDs correspond to tweets that comprise the keywords – "COVID" or "COVID19" or "COVID-19" and one or more keywords/terms that refer to online learning TweetIDs_Omicron.txt – Contains 8198 Tweet IDs correspond to tweets that comprise the keywords – "omicron" or "omicron variant" and one or more keywords/terms that refer to online learning TweetIDs_SARS CoV2.txt – Contains 13 Tweet IDs correspond to tweets that comprise the keyword – "SARS-CoV-2" and one or more keywords/terms that refer to online learning TweetIDs_Duplicates_Removed.txt - A collection of 46208 unique Tweet IDs from all the 6 .txt files mentioned above after...

For the academic year of 2024/2025, the University of Oxford was ranked as the best university in the world, with an overall score of 98.5 according the Times Higher Education. The Massachusetts Institute of Technology and Harvard University followed behind. A high number of the leading universities in the world are located in the United States, with the ETH Zürich in Switzerland the highest ranked neither in the United Kingdom nor the U.S.

There were approximately 18.58 million college students in the U.S. in 2022, with around 13.49 million enrolled in public colleges and a further 5.09 million students enrolled in private colleges. The figures are projected to remain relatively constant over the next few years.

What is the most expensive college in the U.S.? The overall number of higher education institutions in the U.S. totals around 4,000, and California is the state with the most. One important factor that students – and their parents – must consider before choosing a college is cost. With annual expenses totaling almost 78,000 U.S. dollars, Harvey Mudd College in California was the most expensive college for the 2021-2022 academic year. There are three major costs of college: tuition, room, and board. The difference in on-campus and off-campus accommodation costs is often negligible, but they can change greatly depending on the college town.

The differences between public and private colleges Public colleges, also called state colleges, are mostly funded by state governments. Private colleges, on the other hand, are not funded by the government but by private donors and endowments. Typically, private institutions are  much more expensive. Public colleges tend to offer different tuition fees for students based on whether they live in-state or out-of-state, while private colleges have the same tuition cost for every student.

This table gives an overview of expenditure on regular education within the Netherlands.

The government finances schools, colleges and universities. It pays for research which is done by universities on its behalf. Furthermore it provides student grants and loans, allowances for school costs, provisions for students with a disability and child care allowances as well as subsidies to companies and non-profit organisations. The government reclaims unjustified payments for student grants and loans and allowances for school costs. It also receives interest and repayments on student loans as well as EU subsidies for education.

Parents and/or students have to pay tuition fees for schools, colleges and universities, parent contributions and contributions for school activities. They also have to purchase books and materials, pay for transport from home to school and back for students who are not eligible for subsidised transport, pay for private tutoring, pay interest and repayments on student loans, and repay wrongfully received student grants, loans and allowances for school costs. Parents and/or students receive child care allowances, provisions for students with a disability and an allowance for school costs as well as student grants and loans and scholarships of companies.

Companies and non-profit organisations incur costs for supervising trainees and apprentices who combine learning with work experience. They also contribute to the cost of work related education of their employees and spend money on research that is outsourced to colleges for higher professional education and universities. Furthermore they contribute to the childcare allowances given to households and provide scholarships to students. Companies receive subsidies and tax benefits for the creation of apprenticeship places and trainee placements and for providing transport for pupils.

Organisations abroad contract universities in the Netherlands to undertake research for them. The European Union provides funds and subsidies for education to schools, colleges and universities as well as to the Dutch government. Foreign governments contribute to international schools in the Netherlands that operate under their nationality.

The table also contains various indicators used nationally and internationally to compare expenditure on education and place it in a broader context. The indicators are compounded on the basis of definitions of Statistics Netherlands and/or the OECD (Organisation for Economic Cooperation and Development). All figures presented have been calculated according to the standardised definitions of the OECD.

In this table tertiary education includes research and development, except for the indicator Expenditure on education institutions per student, excluding R&D.

The statistic on Education spending is compiled on a cash basis. This means that the education expenditure and revenues are allocated to the year in which they are paid out or received. However, the activity or transaction associated with the payment or receipt can take place in a different year.

Statistics Netherlands published the revised National Accounts in June 2024. Among other things, GDP and total government expenditures have been adjusted upwards as a result of the revision.

Data available from: 1995

Status of the figures: The figures from 1995 to 2022 are final. The 2023 figures are provisional.

Changes as of 31 December 2024: The final figures of 2021 and 2022 and the provisional figures of 2023 have been added. As a result of the revision of the National Accounts, among other things, GDP and total government expenditures have been adjusted upwards. The indicators in this table that are expressed as a percentage of GDP and total government expenditure have been updated for the entire time series from 1995 on the basis of the revised figures.

When will new figures be published? The final figures for 2023 and the provisional figures for 2024 will be published in December 2025. More information on the revision policy of National Accounts can be found under 'relevant articles' under paragraph 3.

An Taisce, in co-operation with the Local Authorities, run the national Green Schools 9-flag program. Schools carry out a number of tasks, run educational programs and environmental projects which are incorporated into everyday school-life. The themes are:Litter & Waste ,Energy, Water, Travel, Biodiversity, Global Citizenship Litter & Waste, Global Citizenship Energy ,Global Citizenship Marine Environment, Global Citizenship TravelThere are 145 schools in Fingal registered as active in the An Taisce Green Schools Program. Got to www.fingal.ie for more information on Green Schools Programme details.. Fingal County Council invests in a number of school projects every year.Each flag has to be maintained as the school progresses towards the new flags. Fingal County Council assists the schools by providing information and support and environmental presentations to the students. In Feb/ March we also carry out all the Green Flag Assessments.We congratulate all the schools on their efforts – the green schools coordinators and committees, the teachers and students and caretakers and all involved put in a lot of work to make this possible.

This dataset contains Version 3.2 of The Global Precipitation Climatology Project (GPCP) Daily Precipitation Data Set (GPCPDAY). The variables included are precipitation and probability of liquid phase.

As the follow-on to the GPCP Version 2.3 Monthly product and the Version 1.3 One Degree Daily product, GPCP Version 3 (GPCP V3.2) seeks to continue the long, homogeneous precipitation record using modern merging techniques and input data sets. The GPCP V3 suite currently consists of the 0.5-degree Monthly and 0.5-degree Daily. A follow-on 0.1-degree 3-hourly product is expected. All GPCPV3 products are constructed to be internally consistent. The Daily product spans June 2000 to the present, with roughly quarterly updates.

Inputs consist of Global Precipitation Measurement (GPM) mission Integrated Multi-satellitE Retrievals for GPM (IMERG) estimates in the span 55 degrees North-South, and Television-Infrared Operational Satellite (TIROS) Operational Vertical Sounder / Advanced Infrared Sounder (TOVS/AIRS) estimates, adjusted climatologically to IMERG across the 55-degree latitude boundary, outside 55 degrees North-South. The Daily estimates are scaled to approximately sum to the Monthly value at each 0.5 degree grid box. In addition to the complete precipitation field, probability of liquid phase estimates are provided globally.

GPCP Version 3 Daily was developed primarily under a current NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program (PI: Ali Behrangi, University of Arizona). The GPCP is computed as a contribution to the World Climate Research Program (WCRP) and Global Water and Energy Exchange (GEWEX) activities, and is part of the array of data sets describing the water and energy cycles of the planet. Prior versions of the GPCP analysis have been produced by a consortium of individual scientists at various government and university institutions and most recently as part of the NOAA Climate Data Record (CDR) Program. The current GPCP Daily product described here creates a global daily satellite product using the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) half-hourly Final Run product at low and middle latitudes, and daily retrievals from Television InfraRed Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) and Atmospheric Infrared Sounder (AIRS) at high latitudes, with calibration to the GPCP V3.2 Monthly SG product.

For more information, see the GPCPDAY official site at NASA GES DISC [https://disc.gsfc.nasa.gov/datasets/GPCPDAY_3.2/summary].

Reefs at Risk Revisited is a high-resolution update of the original global analysis, Reefs at Risk: A Map-Based Indicator of Threats to the World’s Coral Reefs. Reefs at Risk Revisited uses a global map of coral reefs at 500-m resolution, which is 64 times more detailed than the 4-km resolution map used in the 1998 analysis, and benefits from improvements in many global data sets used to evaluate threats to reefs (most threat data are at 1 km resolution, which is 16 times more detailed than those used in the 1998 analysis).

Like the original Reefs at Risk, this study evaluates threats to coral reefs from a wide range of human activities. For the first time, it also includes an assessment of climate-related threats to reefs. In addition, Reefs at Risk Revisited includes a global assessment of the vulnerability of nations and territories to coral reef degradation, based on their dependence on coral reefs and their capacity to adapt.

WRI led the Reefs at Risk Revisited analysis in collaboration with a broad partnership of more than 25 research, conservation, and educational organizations. Partners have provided data, offered guidance on the analytical approach, contributed to the report, and served as critical reviewers of the maps and findings.

Reefs at Risk Revisited is a project of the World Resources Institute (WRI), developed and implemented in close collaboration with: The Nature Conservancy (TNC) WorldFish Center International Coral Reef Action Network (ICRAN) United Nations Environment Programme - World Conservation Monitoring Centre (UNEP-WCMC) Global Coral Reef Monitoring Network (GCRMN)

Many other government agencies, international organizations, research institutions, universities, non-governmental organizations and initiatives provided scientific guidance, contributed data, and reviewed results, including: Atlantic and Gulf Rapid Reef Assessment (AGRRA) Coastal Oceans Research and Development in the Indian Ocean (CORDIO) Conservation International (CI) Coral Reef Alliance (CORAL) Healthy Reefs for Healthy People International Society for Reef Studies (ISRS) International Union for the Conservation of Nature (IUCN) L’Institut de Recherche pour le Developpement (IRD) National Center for Ecological Analysis and Synthesis (NCEAS) Oceana Planetary Coral Reef Foundation Project AWARE Foundation Reef Check Reef Environmental Education Foundation (REEF) SeaWeb Secretariat of the Pacific Community (SPC) Secretariat of the Pacific Regional Environment Programme (SPREP) U.S. National Aeronautics and Space Administration (NASA) U.S. National Oceanic and Atmospheric Administration (NOAA) University of South Florida (USF) University of the South Pacific (USP) Wildlife Conservation Society (WCS) World Wildlife Fund (WWF)

Financial Support: The Chino Cienega Foundation The David and Lucile Packard Foundation The Henry Foundation International Coral Reef Initiative National Fish and Wildlife Foundation Netherlands Ministry of Foreign Affairs The Ocean Foundation Roy Disney Family Foundation The Tiffany & Co. Foundation U.S. Department of the Interior U.S. Department of State

The Global Roads Open Access Data Set, Version 1 (gROADSv1) was developed under the auspices of the CODATA Global Roads Data Development Task Group. The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. All country road networks have been joined topologically at the borders, and many countries have been edited for internal topology. Source data for each country are provided in the documentation, and users are encouraged to refer to the readme file for use constraints that apply to a small number of countries. Because the data are compiled from multiple sources, the date range for road network representations ranges from the 1980s to 2010 depending on the country (most countries have no confirmed date), and spatial accuracy varies. The baseline global data set was compiled by the Information Technology Outreach Services (ITOS) of the University of Georgia. Updated data for 27 countries and 6 smaller geographic entities were assembled by Columbia University's Center for International Earth Science Information Network (CIESIN), with a focus largely on developing countries with the poorest data coverage.

Hip fractures are a major cause of morbidity and mortality in the elderly, and incur high health and social care costs. Given projected population ageing, the number of incident hip fractures is predicted to increase globally. As fracture classification strongly determines the chosen surgical treatment, differences in fracture classification influence patient outcomes and treatment costs. We aimed to create a machine learning method for identifying and classifying hip fractures, and to compare its performance to experienced human observers. We used 3659 hip radiographs, classified by at least two expert clinicians. The machine learning method was able to classify hip fractures with 19% greater accuracy than humans, achieving overall accuracy of 92%.

This data set contains the source data for figures 2 and 4, which are the main Results figures. Data are given in both csv and MAT file formats. The MATLAB scripts for generating the figures are also provided.

Through the efforts of University of Michigan faculty-curators and staff, the Museum of Zoology (UMMZ) develops and maintains excellent zoological collections explicitly for use in research and education benefiting science, society, and the university. The faculty staff and various graduate students of the Birds Division conduct research on the phylogenetic relationships, evolution and behavior of a wide variety of birds using museum collections, molecular techniques, and captive and field studies. The skins collection consists of approximately 176,000 specimens (6th largest in North America; about 2/3 of the species of the world), housed in 224 cases (133 double, 190 single, 1 triple), approximately 5000 sq. feet. In addition to study skins, the collection also includes other special preparations such as 1457 flat pelts and 636 spread wings and tails. The skin collection contains 317 holotypes and 3 lectotypes. These are not available for loan. An annotated list is available (UMMZ Misc. Publ. No. 174, R. W. Storer, 1988). A list and images of our types are available electronically. Extinct species are represented also. The skeletons collection represents approximately 23,800 specimens, 3,500 species (4th largest in the world in number of specimens, 2nd in number of species after the U.S. National Museum) stored in 60 cases, approximately 700 sq. feet. The only families missing are: Mesoenatidae, Rhynochetidae, Dromadidae, Leptosomatidae, Oxyrunchidae, Acanthisittidae, Atrichornithidae, Hyposittidae, and Callaeidae. About 1/2 of the skeleton specimens are from the United States. Other geographic areas for which we have larger collections include: Argentina (830), Australia (485), Canada (435), Costa Rica (1000), Egypt (1000), Ghana (490), Mexico (650), New Caledonia (140), Paraguay (1160), Philippines (340), Surinam (370), and Tanzania (340). Additional collections include: Eggs and nests: 4,600 egg sets and 573 nests in 26 cases. Fluid collection: 4,250 specimens (18th largest in the world), 464 linear feet of shelving and 27 linear feet of floor space for tanks. Also available are 1100 fluid preserved syrinxes. Frozen tissues: Samples from about 4,800 individuals stored in an ultra-cold freezer.

The Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines at high spatial resolution. Dams have multiple attributes, such as name of the dam and impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). While the main focus was to include all dams associated with reservoirs that have a storage capacity of more than 0.1 cubic kilometers, many smaller dams and reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.The Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines at high spatial resolution. Dams have multiple attributes, such as name of the dam and impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). While the main focus was to include all dams associated with reservoirs that have a storage capacity of more than 0.1 cubic kilometers, many smaller dams and reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.

The Comparative Political Economy Database (CPEDB) began at the Centre for Learning, Social Economy and Work (CLSEW) at the Ontario Institute for Studies in Education at the University of Toronto (OISE/UT) as part of the Changing Workplaces in a Knowledge Economy (CWKE) project. This data base was initially conceived and developed by Dr. Wally Seccombe (independent scholar) and Dr. D.W. Livingstone (Professor Emeritus at the University of Toronto). Seccombe has conducted internationally recognized historical research on evolving family structures of the labouring classes (A Millennium of Family Change: Feudalism to Capitalism in Northwestern Europe and Weathering the Storm: Working Class Families from the Industrial Revolution to the Fertility Decline). Livingstone has conducted decades of empirical research on class and labour relations. A major part of this research has used the Canadian Class Structure survey done at the Institute of Political Economy (IPE) at Carleton University in 1982 as a template for Canadian national surveys in 1998, 2004, 2010 and 2016, culminating in Tipping Point for Advanced Capitalism: Class, Class Consciousness and Activism in the ‘Knowledge Economy’ (https://fernwoodpublishing.ca/book/tipping-point-for-advanced-capitalism) and a publicly accessible data base including all five of these Canadian surveys (https://borealisdata.ca/dataverse/CanadaWorkLearningSurveys1998-2016). Seccombe and Livingstone have collaborated on a number of research studies that recognize the need to take account of expanded modes of production and reproduction. Both Seccombe and Livingstone are Research Associates of CLSEW at OISE/UT. The CPEDB Main File (an SPSS data file) covers the following areas (in order): demography, family/household, class/labour, government, electoral democracy, inequality (economic, political & gender), health, environment, internet, macro-economic and financial variables. In its present form, it contains annual data on 725 variables from 12 countries (alphabetically listed): Canada, Denmark, France, Germany, Greece, Italy, Japan, Norway, Spain, Sweden, United Kingdom and United States. A few of the variables date back to 1928, and the majority date from 1960 to 1990. Where these years are not covered in the source, a minority of variables begin with more recent years. All the variables end at the most recent available year (1999 to 2022). In the next version developed in 2025, the most recent years (2023 and 2024) will be added whenever they are present in the sources’ datasets. For researchers who are not using SPSS, refer to the Chart files for overviews, summaries and information on the dataset. For a current list of the variable names and their labels in the CPEDB data base, see the excel file: Outline of SPSS file Main CPEDB, Nov 6, 2023. At the end of each variable label in this file and the SPSS datafile, you will find the source of that variable in a bracket. If I have combined two variables from a given source, the bracket will begin with WS and then register the variables combined. In the 14 variables David created at the beginning of the Class Labour section, you will find DWL in these brackets with his description as to how it was derived. The CPEDB’s variables have been derived from many databases; the main ones are OECD (their Statistics and Family Databases), World Bank, ILO, IMF, WHO, WIID (World Income Inequality Database), OWID (Our World in Data), Parlgov (Parliaments and Governments Database), and V-Dem (Varieties of Democracy). The Institute for Political Economy at Carleton University is currently the main site for continuing refinement of the CPEDB. IPE Director Justin Paulson and other members are involved along with Seccombe and Livingstone in further development and safe storage of this updated database both at the IPE at Carleton and the UT dataverse. All those who explore the CPEDB are invited to share their perceptions of the entire database, or any of its sections, with Seccombe generally (wseccombe@sympatico.ca) and Livingstone for class/labour issues (davidlivingstone@utoronto.ca). They welcome any suggestions for additional variables together with their data sources. A new version CPEDB will be created in the spring of 2025 and installed as soon as the revision is completed. This revised version is intended to be a valuable resource for researchers in all of the included countries as well as Canada.

The number of children, youth and adults not attending schools or universities because of COVID-19 is soaring. Governments all around the world have closed educational institutions in an attempt to contain the global pandemic.

According to UNESCO monitoring, over 100 countries have implemented nationwide closures, impacting over half of world’s student population. Several other countries have implemented localized school closures and, should these closures become nationwide, millions of additional learners will experience education disruption.

This dataset shows the global distribution of coral reefs in tropical and subtropical regions. It is the most comprehensive global dataset of warm-water coral reefs to date, acting as a foundation baseline map for future, more detailed, work. This dataset was compiled from a number of sources by UNEP World Conservation Monitoring Centre (UNEP-WCMC) and the WorldFish Centre, in collaboration with WRI (World Resources Institute) and TNC (The Nature Conservancy). Data sources include the Millennium Coral Reef Mapping Project (IMaRS-USF and IRD 2005, IMaRS-USF 2005) and the World Atlas of Coral Reefs (Spalding et al. 2001).

Citation: UNEP-WCMC, WorldFish Centre, WRI, TNC (2018). Global distribution of warm-water coral reefs, compiled from multiple sources including the Millennium Coral Reef Mapping Project. Version 4.0. Includes contributions from IMaRS-USF and IRD (2005), IMaRS-USF (2005) and Spalding et al. (2001). Cambridge (UK): UN Environment World Conservation Monitoring Centre. URL: http://data.unep-wcmc.org/datasets/1

Citations for the separate entities: IMaRS-USF (Institute for Marine Remote Sensing-University of South Florida) (2005). Millennium Coral Reef Mapping Project. Unvalidated maps. These maps are unendorsed by IRD, but were further interpreted by UNEP World Conservation Monitoring Centre. Cambridge (UK): UNEP World Conservation Monitoring Centre

IMaRS-USF, IRD (Institut de Recherche pour le Developpement) (2005). Millennium Coral Reef Mapping Project. Validated maps. Cambridge (UK): UNEP World Conservation Monitoring Centre

Spalding MD, Ravilious C, Green EP (2001). World Atlas of Coral Reefs. Berkeley (California, USA): The University of California Press. 436 pp.

This dataset of historical poor law cases was created as part of a project aiming to assess the implications of the introduction of Artificial Intelligence (AI) into legal systems in Japan and the United Kingdom. The project was jointly funded by the UK’s Economic and Social Research Council, part of UKRI, and the Japanese Society and Technology Agency (JST), and involved collaboration between Cambridge University (the Centre for Business Research, Department of Computer Science and Faculty of Law) and Hitotsubashi University, Tokyo (the Graduate Schools of Law and Business Administration). As part of the project, a dataset of historic poor law cases was created to facilitate the analysis of legal texts using natural language processing methods. The dataset contains judgments of cases which have been annotated to facilitate computational analysis. Specifically, they make it possible to see how legal terms have evolved over time in the area of disputes over the law governing settlement by hiring.

A World Economic Forum meeting at Davos 2019 heralded the dawn of 'Society 5.0' in Japan. Its goal: creating a 'human-centred society that balances economic advancement with the resolution of social problems by a system that highly integrates cyberspace and physical space.' Using Artificial Intelligence (AI), robotics and data, 'Society 5.0' proposes to '...enable the provision of only those products and services that are needed to the people that need them at the time they are needed, thereby optimizing the entire social and organizational system.' The Japanese government accepts that realising this vision 'will not be without its difficulties,' but intends 'to face them head-on with the aim of being the first in the world as a country facing challenging issues to present a model future society.' The UK government is similarly committed to investing in AI and likewise views the AI as central to engineering a more profitable economy and prosperous society.

This vision is, however, starting to crystallise in the rhetoric of LegalTech developers who have the data-intensive-and thus target-rich-environment of law in their sights. Buoyed by investment and claims of superior decision-making capabilities over human lawyers and judges, LegalTech is now being deputised to usher in a new era of 'smart' law built on AI and Big Data. While there are a number of bold claims made about the capabilities of these technologies, comparatively little attention has been directed to more fundamental questions about how we might assess the feasibility of using them to replicate core aspects of legal process, and ensuring the public has a meaningful say in the development and implementation.

This innovative and timely research project intends to approach these questions from a number of vectors. At a theoretical level, we consider the likely consequences of this step using a Horizon Scanning methodology developed in collaboration with our Japanese partners and an innovative systemic-evolutionary model of law. Many aspects of legal reasoning have algorithmic features which could lend themselves to automation. However, an evolutionary perspective also points to features of legal reasoning which are inconsistent with ML: including the reflexivity of legal knowledge and the incompleteness of legal rules at the point where they encounter the 'chaotic' and unstructured data generated by other social sub-systems. We will test our theory by developing a hierarchical model (or ontology), derived from our legal expertise and public available datasets, for classifying employment relationships under UK law. This will let us probe the extent to which legal reasoning can be modelled using less computational-intensive methods such as Markov Models and Monte Carlo Trees.

Building upon these theoretical innovations, we will then turn our attention from modelling a legal domain using historical data to exploring whether the outcome of legal cases can be reliably predicted using various technique for optimising datasets. For this we will use a data set comprised of 24,179 cases from the High Court of England and Wales. This will allow us to harness Natural Language Processing (NLP) techniques such as named entity recognition (to identify relevant parties) and sentiment analysis (to analyse opinions and determine the disposition of a party) in addition to identifying the main legal and factual points of the dispute, remedies, costs, and trial durations. By trailing various predictive heuristics and ML techniques against this dataset we hope to develop a more granular understanding as to the feasibility of predicting dispute outcomes and insight to what factors are relevant for legal decision-making. This will allow us to then undertake a comparative analysis with the results of existing studies and shed light on the legal contexts and questions where AI can and cannot be used to produce accurate and repeatable results.

The Global Flood Proportional Economic Loss Risk Deciles is a 2.5 minute grid of flood hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical Unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

The Global Cyclone Mortality Risks and Distribution is a 2.5 minute grid of global cyclone mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality loss. Mortality loss estimates per hazard event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of cyclone hazard are obtained from the Global Cyclone Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of an approximately equal number of grid cells, providing a relative estimate of cyclone-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

A global dataset of meteorological forcings has been developed that can be used to drive models of land surface hydrology. The dataset is constructed by combining a suite of global observation-based datasets with the NCEP/NCAR reanalysis. Known biases in the reanalysis precipitation and near-surface meteorology have been shown to exert an erroneous effect on modeled land surface water and energy budgets and are thus corrected using observation-based datasets of precipitation, air temperature and radiation. Corrections are also made to the rain day statistics of the reanalysis precipitation which have been found to exhibit a spurious wave-like pattern in high-latitude wintertime. Wind-induced low measurement of solid precipitation is removed using the results from the World Meteorological Organization (WMO) Solid Precipitation Measurement Intercomparison. Precipitation is disaggregated in space to 1.0 degree and 0.25 degree by statistical downscaling using relationships developed with the Global Precipitation Climatology Project (GPCP) daily product. Disaggregation in time from daily to 3-hourly is accomplished similarly, using the Tropical Rainfall Measuring Mission (TRMM) 3-hourly real-time dataset. Other meteorological variables (downward short- and longwave, specific humidity, surface air pressure and wind speed) are downscaled in space with account for changes in elevation. The dataset is evaluated against the bias-corrected forcing dataset of the second Global Soil Wetness Project. The final product provides a long-term, globally-consistent dataset of near-surface meteorological variables that can be used to drive models of the terrestrial hydrologic and ecological processes for the study of seasonal and interannual variability and for the evaluation of coupled models and other land surface prediction schemes.