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Description: Monthly revenue generated by conveyances of real property over $5 million, from when applicable transfer tax collection began on April 1, 2023 to present. Consistent with the ULA ordinance, the property sale value thresholds and their corresponding tax rates will be adjusted annually based on the Bureau of Labor Statistics Chained Consumer Price Index.

***Starting on March 7th, 2024, the Los Angeles Police Department (LAPD) will adopt a new Records Management System for reporting crimes and arrests. This new system is being implemented to comply with the FBI's mandate to collect NIBRS-only data (NIBRS — FBI - https://www.fbi.gov/how-we-can-help-you/more-fbi-services-and-information/ucr/nibrs). During this transition, users will temporarily see only incidents reported in the retiring system. However, the LAPD is actively working on generating new NIBRS datasets to ensure a smoother and more efficient reporting system. *** **Update 1/18/2024 - LAPD is facing issues with posting the Crime data, but we are taking immediate action to resolve the problem. We understand the importance of providing reliable and up-to-date information and are committed to delivering it. As we work through the issues, we have temporarily reduced our updates from weekly to bi-weekly to ensure that we provide accurate information. Our team is actively working to identify and resolve these issues promptly. We apologize for any inconvenience this may cause and appreciate your understanding. Rest assured, we are doing everything we can to fix the problem and get back to providing weekly updates as soon as possible. ** This dataset reflects incidents of crime in the City of Los Angeles dating back to 2020. This data is transcribed from original crime reports that are typed on paper and therefore there may be some inaccuracies within the data. Some location fields with missing data are noted as (0°, 0°). Address fields are only provided to the nearest hundred block in order to maintain privacy. This data is as accurate as the data in the database. Please note questions or concerns in the comments.

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

This parcels polygons feature class represents current city parcels within the City of Los Angeles. It shares topology with the Landbase parcel lines feature class. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most current geographic information of the public right of way, ownership and land record information. The legal boundaries are determined on the ground by license surveyors in the State of California, and by recorded documents from the Los Angeles County Recorder's office and the City Clerk's office of the City of Los Angeles. Parcel and ownership information are available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Associated information about the landbase parcels is entered into attributes. Principal attributes include:PIN and PIND: represents the unique auto-generated parcel identifier and key to related features and tables. This field is related to the LA_LEGAL, LA_APN and LA_HSE_NBR tables. PIN contains spaces and PIND replaces those spaces with a dash (-).LA_LEGAL - Table attributes containing legal description. Principal attributes include the following:TRACT: The subdivision tract number as recorded by the County of Los AngelesMAP_REF: Identifies the subdivision map book reference as recorded by the County of Los Angeles.LOT: The subdivision lot number as recorded by the County of Los Angeles.ENG_DIST: The four engineering Districts (W=Westla, C=Central, V= Valley and H=Harbor).CNCL_DIST: Council Districts 1-15 of the City of Los Angeles. OUTLA means parcel is outside the City.LA_APN- Table attributes containing County of Los Angeles Assessors information. Principal attributes include the following:BPP: The Book, Page and Parcel from the Los Angeles County Assessors office. SITUS*: Address for the property.LA_HSE_NBR - Table attributes containing housenumber information. Principal attributes include the following:HSE_ID: Unique id of each housenumber record.HSE_NBR: housenumber numerical valueSTR_*: Official housenumber addressFor a complete list of attribute values, please refer to Landbase_parcel_polygons_data_dictionary.Landbase parcels polygons data layer was created in geographical information systems (GIS) software to display the location of the right of way. The parcels polygons layer delineates the right of way from Landbase parcels lots. The parcels polygons layer is a feature class in the LACityLandbaseData.gdb Geodatabase dataset. The layer consists of spatial data as a polygon feature class and attribute data for the features. The area inside a polygon feature is a parcel lot. The area outside of the parcel polygon feature is the right of way. Several polygon features are adjacent, sharing one line between two polygons. For each parcel, there is a unique identifier in the PIND and PIN fields. The only difference is PIND has a dash and PIN does not. The types of edits include new subdivisions and lot cuts. Associated legal information about the landbase parcels lots is entered into attributes. The landbase parcels layer is vital to other City of LA Departments, by supporting property and land record operations and identifying legal information for City of Los Angeles. The landbase parcels polygons are inherited from a database originally created by the City's Survey and Mapping Division. Parcel information should only be added to the Landbase Parcels layer if documentation exists, such as a Deed or a Plan approved by the City Council. When seeking the definitive description of real property, consult the recorded Deed or Plan.List of Fields:ID: A unique numeric identifier of the polygon. The ID value is the last part of the PIN field value.ASSETID: User-defined feature autonumber.MAPSHEET: The alpha-numeric mapsheet number, which refers to a valid B-map or A-map number on the Cadastral grid index map. Values: • B, A, -5A - Any of these alpha-numeric combinations are used, whereas the underlined spaces are the numbers. An A-map is the smallest grid in the index map and is used when there is a large amount of spatial information in the map display. There are more parcel lines and annotation than can fit in the B-map, and thus, an A-map is used. There are 4 A-maps in a B-map. In areas where parcel lines and annotation can fit comfortably in an index map, a B-map is used. The B-maps are at a scale of 100 feet, and A-maps are at a scale of 50 feet.OBJECTID: Internal feature number.BPPMAP_REFTRACTBLOCKMODLOTARBCNCL_DIST: LA City Council District. Values: • (numbers 1-15) - Current City Council Member for that District can be found on the mapping website http://navigatela.lacity.org/navigatela, click Council Districts layer name, under Boundaries layer group.SHAPE: Feature geometry.BOOKPAGEPARCELPIND: The value is a combination of MAPSHEET and ID fields, creating a unique value for each parcel. The D in the field name PIND, means "dash", and there is a dash between the MAPSHEET and ID field values. This is a key attribute of the LANDBASE data layer. This field is related to the APN and HSE_NBR tables.ENG_DIST: LA City Engineering District. The boundaries are displayed in the Engineering Districts index map. Values: • H - Harbor Engineering District. • C - Central Engineering District. • V - Valley Engineering District. • W - West LA Engineering District.PIN: The value is a combination of MAPSHEET and ID fields, creating a unique value for each parcel. There are spaces between the MAPSHEET and ID field values. This is a key attribute of the LANDBASE data layer. This field is related to the APN and HSE_NBR tables.

School level revenue balances for all local authority maintained schools by local authority.

These tatistics describing the total revenue balances (showing both committed and uncommitted revenue balances) and also the total revenue balances as a proportion of the total revenue income for LA maintained schools.

Tables from 1990/00 onwards available on Department for Education website.

A schools total revenue income for the year includes all revenue funding available to the year as well as any additional income generated by the school. This does not include any revenue balances carried forward from previous years.

Please note that although figures are shown here for committed and uncommitted balances there is considerable variation in how these are defined at local level meaning that at national level this comparison is not consistent or meaningful. Great caution should therefore be used in interpreting these figures.

The tables form part of annual series of school balances statistics which have been published on the Department's Every Child Matters website.

This dataset is about book subjects. It has 4 rows and is filtered where the books is All we want is make us free : La Amistad and the reform abolitionists. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Water abstraction zones in the natural environment for the purpose of supplying drinking water to human communities (Articles L 1321-2 and L1321-2-1 of the Code de la santé publique).The generator of a public utility easement is a geographical entity whose nature or function induces, by virtue of regulations, constraints on the way the land is occupied on the surrounding land.The disappearance or destruction on the site of the generator does not remove the easement or easements associated with it. Only a new act of annulment or repeal by the competent authority may legally remove the effects of the easement(s) in question.

This is a supporting dataset that accompanies arXiv:1704.05309. It consists of databases and parameter files used to generate the numerical results presented in the paper.

This deposit contains the following files:

Databases

These are SQLite databases produced by the Sussex LSSEFT tool (git revision 977e5b03) containing the one-loop SPT results and counterterms used to obtain EFT predictions for the real-space and redshift-space power spectra.

• Planck2015_CAMB@z=0_k10-full.sqlite
  This contains results for a Planck2015 cosmology. The initial power spectrum is generated using linear theory by CAMB at z = 50, using the parameter file documented below. The tree-level component is generated using a CAMB z = 0 power spectrum, also computed using linear theory, for which we also supply a CAMB parameter file.
• MDR1_CAMB@z=0_k10-full.sqlite
  This contains results for a cosmology matching the MultiDark MDR1 simulation. The initial power spectrum is generated using linear theory by CAMB at z = 50 and the tree-level component is generated using a CAMB z = 0 power spectrum as above. The corresponding parameter files are supplied; see below.
• MDR1_CAMB@z=0_k10-EdS.sqlite
  This contains results matching the MDR1_CAMB@z=0_k10-full.sqlite database, but with growth functions computed using the standard SPT Einstein-de Sitter approximation.

CAMB parameter files

Power spectra were produced using the November 2015 release of CAMB.

• Planck2015_linear_init_params.ini, Planck2015_linear_final_params.ini
  Parameter files to generate initial (z = 50) and final (z = 0) linear power spectra in the Planck2015 cosmology.
• Planck2015_nonlinear_params.ini
  Parameter file to generate a nonlinear z = 0 power spectrum using the HALOFIT prescription for the Planck2015 cosmology. This is used to renormalize the EFT power spectrum.
• MDR1_linear_init_params.ini, MDR1_linear_final_params.ini
  Parameter files to generate initial (z = 50) and final (z = 0) linear power spectra in the MDR1 cosmology.

gevolution parameter files

N-body simulations were performed using gevolution 1.1.

• gevolutionsettings.ini
  This is a settings file for gevolution that reproduces our 1024^3-particle, (2000 Mpc/h)-side simulation volume, used to compute the real-space power spectrum and the l = 0, 2, 4 multipoles of the redshift-space power spectrum.

This physical structures points feature class represents current wastewater information in the City of Los Angeles. The maintenance hole structure is used to provide access to the sewer from the surface. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most rigorous geographic information of the sanitary sewer system using a geometric network model, to ensure that its sewers reflect current ground conditions. The sanitary sewer system, pump plants, wyes, maintenance holes, and other structures represent the sewer infrastructure in the City of Los Angeles. Wye and sewer information is available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Associated information about the wastewater Physical_structure is entered into attributes. Principal attributes include:JUNCTION_SUBTYPE: junction subtype is the principal field that describes various types of points as either Diversion Structure, Drop or Drop Trap, Flush, Junction Chamber, Junction Structure, Maintenance, Offset, Other Structure, Siphon, Special Shallow, Special Structure, Terminal, Transition, Trap, Valve Vault, Weir. For a complete list of attribute values, please refer to (TBA Wastewater data dictionary). Wastewater Physical Structures points layer was created in geographical information systems (GIS) software to display the location of wastewater structures. The structures points layer is a feature class in the LACityWastewaterData.gdb Geodatabase dataset. The layer consists of spatial data as a points feature class and attribute data for the features. The points are entered manually based on wastewater sewer maps and BOE standard plans, and information about the points is entered into attributes. The physical structures points data layer differs from non-structures points data layer, such that physical structures points are maintenance holes. Reference the JUNCTION_SUBTYPE and MH_TYPE field for the type of structure. The STRUCTURE_ID field value is the unique ID. The wastewater structures points are inherited from a sewer spatial database originally created by the City's Wastewater program. The database was known as SIMMS, Sewer Inventory and Maintenance Management System. Structures information should only be added to the Wastewater Structures layer if documentation exists, such as a wastewater map approved by the City Engineer. Sewers plans and specifications proposed under private development are reviewed and approved by Bureau of Engineering. The Department of Public Works, Bureau of Engineering's, Brown Book (current as of 2010) outlines standard specifications for public works construction. For more information on sewer materials and structures, look at the Bureau of Engineering Manual, Part F, Sewer Design section, and a copy can be viewed at http://eng.lacity.org/techdocs/sewer-ma/f400.pdf. For more information on maintenance holes, a copy can be viewed at http://boemaps.eng.ci.la.ca.us/reports/pdf/s140-0_std_pl.pdf.List of Fields:SERVICEID: User-defined unique feature number that is automatically generated.OBJECTID: Internal feature number.FACILITY_NO: This field is currently not being edited.ENG_DIST: LA City Engineering District. The boundaries are displayed in the Engineering Districts index map. Values: • W - West LA Engineering District. • H - Harbor Engineering District. • C - Central Engineering District. • V - Valley Engineering District.CNCL_DIST: LA City Council District. Values: • (numbers 1-15) - Current City Council Member for that District can be found on the mapping website http://navigatela.lacity.org/index.cfm, click Council Districts layer name, under Boundaries layer group.CRTN_DT: Creation date of the point feature.MDIST: This value is the maintenance district identifier. Bureau of Sanitation needs to provide BOE with updated definitions. This field is currently not being edited.LAT: The value is the latitude coordinate of the point.USER_ID: The name of the user carrying out the edits of the structure data.LON: The value is the longitude coordinate of the point.NAME: This field is currently not being edited.VDATUM: This is the year of the standard plan, which contains the information the user enters into pipe data.MHMATERIAL: The value is the material that the structure is made from. This information is not specified on the standard plan. Values: • UNK - Unknown. • RCP - Reinforced Concrete Pipe. • CSP - Corrugated Steel Pipe. • CIPC - Cast in place concrete. • C - Concrete. • BRK - Brick. • PRC - Precast Reinforced Concrete. • B - Brick. • CON - Concrete. • VCP - Vitrified Clay Pipe. • O - Other. • P - Plastic.BLKNO: The value is the block number of the street on which the physical structure is located.STREET2: The value is the cross street name on which the physical structure hole is located, if applicable.COVERDIAM: The value diameter of the physical structure cover expressed in feet.BARRELDIAM: The value diameter of the inside of the physical structure expressed in feet.STATUS: This value is the active or inactive status of the structure. Values: • ABAN - Proposed Inactive. • PROP_ACT - Proposed Active. • INACT - Inactive. • ACT - Active. • ABAN - Abandoned.SEQ: The value is the sequence number of the maintenance hole.SHAPE: Feature geometry.STREET1: The value is the street name on which the physical structure is located.MH_BASE: The value is the non-structure base, used by Bureau of Sanitation to describe the direction of flow at the intersection of a pipe and a non-structure. Values: • F - F. • B - B. • G - G. • H - H. • Q - Q.MH_TYPE: The value signifies the maintenance hole type or other structure type. Values: • DMH - Drop Maintenance Hole. • CFS - Confluence Structure. • DMT - Drop trap Maintenance Hole. • ABN - Abandoned. • BPS - Bypass Structure. • DI - Diversion Structure. • SH - Shallow Maintenance Hole. • OMH - Offset Maintenance Hole. • RV - Relief Valve. • SIP - Siphon. • VV - Valve vault. • LH - Lamp Hole. • FL - Flush Station. • GV - Gate Valve. • TRP - Trap maintenance hole. This type of structure is used to prevent sewer gases from flowing upstream in the sewer line. • HD - Transition. • TRS - Transition structure. • FT - Flush Tank. • WMH - Weir maintenance hole. This type of structure is used to gauge sewer flows. Automatic recording devices may be installed for flow measurement. • INA - Inactive. • MH - Maintenance Hole. • OTH - Other structure. • FS - Flush Station. • WW - Wet well. • JT - Junction Chamber Trap. • JC - Junction Chamber. • PMH - Pressure Maintenance Hole. • PS - Pump Station. • FMH - Flush Maintenance Hole. • TMH - Terminal maintenance hole. • GS - Gauging Structure. • JS - Junction Structure.LID_ELEV: The value is the lid elevation of the structure, in decimal feet.BASIN: The value is basin number.OWNER: This value is the agency or municipality that constructed the physical structure. Values: • CTY - City of LA. • FED - Federal Facilities. • OUTLA - Adjoining cities. • COSA - LA County Sanitation. • PVT - Private.COMMENTS: This attribute contains comments of structures and structure status.MH_DEPTH: The value is the depth of the physical structure expressed in decimal feet.JUNCTION_SUBTYPE: The value is the type of physical structure. Values: • 1 - Maintenance. • 4 - Offset. • 15 - Valve Vault. • 6 - Diversion Structure. • 8 - Flush. • 9 - Junction Chamber. • 5 - Trap. • 7 - Special Shallow. • 3 - Terminal. • 10 - Siphon. • 13 - Junction Structure. • 16 - Transition. • 2 - Drop or Drop Trap. • 11 - Weir. • 12 - Special Structure. • 14 - Other Structure.LAST_UPDATE: Date of last update of the point feature.YEAR_INST: This is the year of the structure installation.ROUTE: The value is the sewer maintenance route number.ADDRESS: This field is currently not being edited.ENABLED: Internal feature number.STRUCTURE_ID: The value is the ID of the structure. It could be either the value from the UP_STRUCT or DN_STRUCT fields. This point is the structure that may be a maintenance hole, junction, siphon, etc. The field STRUCTURE_ID is a key attribute to relate the physical structures feature class to the UP_MH field or the DN_MH field in pipe lines feature class.ASSETID: User-defined unique feature number that is automatically generated.

Money Supply M0 in the United States decreased to 5732900 USD Million in April from 5775200 USD Million in March of 2025. This dataset provides - United States Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The data.gouv.fr platform is the single interministerial portal intended to gather and freely make available all public information of the State, its public administrative bodies, local and regional authorities and persons governed by public or private law entrusted with a public service mission. This dataset contains information about the entire data catalogue of data.gouv.fr: La liste des jeux de données publiés sur data.gouv.fr : for each published dataset, its identification number, its title, its url, its affiliation to an organisation (where it exists), its description, its update frequency, its associated license, its temporal and spatial coverage, its creation and last update dates, its publication status (private or public), its assigned tags and some audience indicators are indicated. La liste des ressources publiées sur data.gouv.fr : for each published resource, it shall include its attachment to a dataset, its title, description, url, type, format, size, creation and last update dates and number of downloads. La liste des réutilisations publiées sur data.gouv.fr : for each re-use published, its identification number, its title, its url, its type, its description, its attachment to an organisation, its creation and last update dates, its assigned tags, its attachment to the re-used dataset and some audience indicators are indicated. La liste des organisations créées sur data.gouv.fr : for each organisation created, it includes its identification, name, url, description, logo, dates of creation and last modification and some audience indicators. La liste des tags créés sur data.gouv.fr : for each tag created is indicated the number of times it has been assigned to a dataset and reuse. La liste des discussions ouvertes sur data.gouv.fr : for each open discussion, it includes its identification number, the name of its linked user, its title, its number of messages, the content of its messages, its creation and closing dates. La liste des moissoneurs sur data.gouv.fr : for each harvester, it is indicated in particular its status (validated or pending), the name of the organization and the technology concerned. The data is collected from a script that automatically extracts the data from the database. They are published under an open license and are updated weekly.

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

Abstract

Estache and Goicoechea present an infrastructure database that was assembled from multiple sources. Its main purposes are: (i) to provide a snapshot of the sector as of the end of 2004; and (ii) to facilitate quantitative analytical research on infrastructure sectors. The related working paper includes definitions, source information and the data available for 37 performance indicators that proxy access, affordability and quality of service (most recent data as of June 2005). Additionally, the database includes a snapshot of 15 reform indicators across infrastructure sectors.

This is a first attempt, since the effort made in the World Development Report 1994, at generating a database on infrastructure sectors and it needs to be recognized as such. This database is not a state of the art output—this is being worked on by sector experts on a different time table. The effort has however generated a significant amount of new information. The database already provides enough information to launch a much more quantitative debate on the state of infrastructure. But much more is needed and by circulating this information at this stage, we hope to be able to generate feedback and fill the major knowledge gaps and inconsistencies we have identified.

Geographic coverage

The database covers the following countries: - Afghanistan - Albania - Algeria - American Samoa - Andorra - Angola - Antigua and Barbuda - Argentina - Armenia - Aruba - Australia - Austria - Azerbaijan - Bahamas, The - Bahrain - Bangladesh - Barbados - Belarus - Belgium - Belize - Benin - Bermuda - Bhutan - Bolivia - Bosnia and Herzegovina - Botswana - Brazil - Brunei - Bulgaria - Burkina Faso - Burundi - Cambodia - Cameroon - Canada - Cape Verde - Cayman Islands - Central African Republic - Chad - Channel Islands - Chile - China - Colombia - Comoros - Congo, Dem. Rep. - Congo, Rep. - Costa Rica - Cote d'Ivoire - Croatia - Cuba - Cyprus - Czech Republic - Denmark - Djibouti - Dominica - Dominican Republic - Ecuador - Egypt, Arab Rep. - El Salvador - Equatorial Guinea - Eritrea - Estonia - Ethiopia - Faeroe Islands - Fiji - Finland - France - French Polynesia - Gabon - Gambia, The - Georgia - Germany - Ghana - Greece - Greenland - Grenada - Guam - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong Kong, China - Hungary - Iceland - India - Indonesia - Iran, Islamic Rep. - Iraq - Ireland - Isle of Man - Israel - Italy - Jamaica - Japan - Jordan - Kazakhstan - Kenya - Kiribati - Korea, Dem. Rep. - Korea, Rep. - Kuwait - Kyrgyz Republic - Lao PDR - Latvia - Lebanon - Lesotho - Liberia - Libya - Liechtenstein - Lithuania - Luxembourg - Macao, China - Macedonia, FYR - Madagascar - Malawi - Malaysia - Maldives - Mali - Malta - Marshall Islands - Mauritania - Mauritius - Mayotte - Mexico - Micronesia, Fed. Sts. - Moldova - Monaco - Mongolia - Morocco - Mozambique - Myanmar - Namibia - Nepal - Netherlands - Netherlands Antilles - New Caledonia - New Zealand - Nicaragua - Niger - Nigeria - Northern Mariana Islands - Norway - Oman - Pakistan - Palau - Panama - Papua New Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Puerto Rico - Qatar - Romania - Russian Federation - Rwanda - Samoa - San Marino - Sao Tome and Principe - Saudi Arabia - Senegal - Seychelles - Sierra Leone - Singapore - Slovak Republic - Slovenia - Solomon Islands - Somalia - South Africa - Spain - Sri Lanka - St. Kitts and Nevis - St. Lucia - St. Vincent and the Grenadines - Sudan - Suriname - Swaziland - Sweden - Switzerland - Syrian Arab Republic - Tajikistan - Tanzania - Thailand - Togo - Tonga - Trinidad and Tobago - Tunisia - Turkey - Turkmenistan - Uganda - Ukraine - United Arab Emirates - United Kingdom - United States - Uruguay - Uzbekistan - Vanuatu - Venezuela, RB - Vietnam - Virgin Islands (U.S.) - West Bank and Gaza - Yemen, Rep. - Yugoslavia, FR (Serbia/Montenegro) - Zambia - Zimbabwe

Kind of data

Aggregate data [agg]

Mode of data collection

Face-to-face [f2f]

Response rate

Sector Performance Indicators

Energy The energy sector is relatively well covered by the database, at least in terms of providing a relatively recent snapshot for the main policy areas. The best covered area is access where data are available for 2000 for about 61% of the 207 countries included in the database. The technical quality indicator is available for 60% of the countries, and at least one of the perceived quality indicators is available for 40% of the countries. Price information is available for about 41% of the countries, distinguishing between residential and non residential.

Water & Sanitation Because the sector is part of the Millennium Development Goals (MDGs), it enjoys a lot of effort on data generation in terms of the access rates. The WHO is the main engine behind this effort in collaboration with the multilateral and bilateral aid agencies. The coverage is actually quite high -some national, urban and rural information is available for 75 to 85% of the countries- but there are significant concerns among the research community about the fact that access rates have been measured without much consideration to the quality of access level. The data on technical quality are only available for 27% of the countries. There are data on perceived quality for roughly 39% of the countries but it cannot be used to qualify the information provided by the raw access rates (i.e. access 3 hours a day is not equivalent to access 24 hours a day).

Information and Communication Technology The ICT sector is probably the best covered among the infrastructure sub-sectors to a large extent thanks to the fact that the International Telecommunications Union (ITU) has taken on the responsibility to collect the data. ITU covers a wide spectrum of activity under the communications heading and its coverage ranges from 85 to 99% for all national access indicators. The information on prices needed to make assessments of affordability is also quite extensive since it covers roughly 85 to 95% of the 207 countries. With respect to quality, the coverage of technical indicators is over 88% while the information on perceived quality is only available for roughly 40% of the countries.

Transport The transport sector is possibly the least well covered in terms of the service orientation of infrastructure indicators. Regarding access, network density is the closest approximation to access to the service and is covered at a rate close to 90% for roads but only at a rate of 50% for rail. The relevant data on prices only cover about 30% of the sample for railways. Some type of technical quality information is available for 86% of the countries. Quality perception is only available for about 40% of the countries.

Institutional Reform Indicators

Electricity The data on electricity policy reform were collected from the following sources: ABS Electricity Deregulation Report (2004), AEI-Brookings telecommunications and electricity regulation database (2003), Bacon (1999), Estache and Gassner (2004), Estache, Trujillo, and Tovar de la Fe (2004), Global Regulatory Network Program (2004), Henisz et al. (2003), International Porwer Finance Review (2003-04), International Power and Utilities Finance Review (2004-05), Kikukawa (2004), Wallsten et al. (2004), World Bank Caribbean Infrastructure Assessment (2004), World Bank Global Energy Sector Reform in Developing Countries (1999), World Bank staff, and country regulators. The coverage for the three types of institutional indicators is quite good for the electricity sector. For regulatory institutions and private participation in generation and distribution, the coverage is about 80% of the 207 counties. It is somewhat lower on the market structure with only 58%.

Water & Sanitation The data on water policy reform were collected from the following sources: ABS Water and Waste Utilities of the World (2004), Asian Developing Bank (2000), Bayliss (2002), Benoit (2004), Budds and McGranahan (2003), Hall, Bayliss, and Lobina (2002), Hall and Lobina (2002), Hall, Lobina, and De La Mote (2002), Halpern (2002), Lobina (2001), World Bank Caribbean Infrastructure Assessment (2004), World Bank Sector Note on Water Supply and Sanitation for Infrastructure in EAP (2004), and World Bank staff. The coverage for institutional reforms in W&S is not as exhaustive as for the other utilities. Information on the regulatory institutions responsible for large utilities is available for about 67% of the countries. Ownership data are available for about 70% of the countries. There is no information on the market structure good enough to be reported here at this stage. In most countries small scale operators are important private actors but there is no systematic record of their existence. Most of the information available on their role and importance is only anecdotal.

Information and Communication Technology The report Trends in Telecommunications Reform from ITU (revised by World Bank staff) is the main source of information for this sector. The information on institutional reforms in the sector is however not as exhaustive as it is for its sector performance indicators. While the coverage on the regulatory institutions is 100%, it varies between 76 and 90% of the countries for more of the other indicators. Quite surprisingly also, in contrast to what is available for other sectors, it proved difficult to obtain data on the timing of reforms and of the creation of the regulatory agencies.

Transport Information on transport institutions and reforms is not systematically generated by any agency. Even though more data are needed to have a more comprenhensive picture of the transport sector, it was possible to collect data on railways policy reform from Janes World Railways (2003-04) and complement it with

Summary: This dataset contains an inventory of City of Los Angeles Sidewalks and related features (Access Ramps, Curbs, Driveways, and Parkways).Background: This inventory was performed throughout 2017 using a combination of G.I.S software, aerial imagery (2014 LARIAC), and a geographic dataset of property/right-of way lines. The dataset has not been updated since its creation.Description: The following provides more detail about the feature classes in this dataset. All features were digitized (“traced”) as observed in the orthophotography (digital aerial photos) and assigned the Parcel Identification Number (PIN) of their corresponding property:Sidewalk (polygon) – represents paved pedestrian walkways. Typical widths are between 3‐6 feet in residential areas and larger and more variable in commercial and high‐density traffic areas.Alley-Sidewalk (polygon) – represents the prevailing walkway or path of travel at the entrance/exit of an alley. Digitized as Sidewalk features but categorized as Alley Sidewalk and assigned a generic PIN value, ALLEY SIDEWALK.Corner Polygon (polygon) - feature created where sidewalks from two streets meet but do not intersect (i.e. at corner lots). There’s no standard shape/type and configurations vary widely. These are part of the Sidewalk feature class.In commercial and high‐density residential areas where there is only continuous sidewalk (no parkway strip), the sidewalk also functions as a Driveway.Driveway (polygon) – represents area that provides vehicular access to a property. Features are not split by extended parcel lot lines except when two adjacent properties are served by the same driveway approach (e.g. a common driveway), in which case they are and assigned a corresponding PIN.Parkway (polygon) – represents the strip of land behind the curb and in front of the sidewalk. Generally, they are landscaped with ground cover but they may also be filled in with decorative stone, pavers, decomposed granite, or concrete. They are created by offsetting lines, the Back of Curb (BOC) line and the Face of Walk (FOW). The distance between the BOC and FOW is measured off the aerial image and rounded to the nearest 0.5 foot, typically 6 – 10 feet.Curb (polygon) – represents the concrete edging built along the street to form part of the gutter. Features are always 6” wide strips and are digitized using the front of curb and back of curb digitized lines. They are the leading improvement polygon and are created for all corner, parkway, driveway and, sidewalk (if no parkway strip is present) features.Curb Ramp, aka Access Ramp (point) – represents the geographic center (centroid) of Corner Polygon features in the Sidewalk feature class. They have either a “Yes” or “No” attribute that indicates the presence or absence of a wheelchair access ramp, respectively.Fields: All features include the following fields...FeatureID – a unique feature identifier that is populated using the feature class’ OBJECTID fieldAssetID – a unique feature identifier populated by Los Angeles City staff for internal usePIND – a unique Parcel Identification Number (PIN) for all parcels within the City of L.A. All Sidewalk related features will be split, non-overlapping, and have one associated Parcel Identification Number (PIN). CreateDate – indicates date feature was createdModifiedDate – indicates date feature was revised/editedCalc_Width (excluding Access Ramps) – a generalized width of the feature calculated using spatial and mathematical algorithms on the feature. In almost all cases where features have variable widths, the minimum width is used. Widths are rounded to the nearest whole number. In cases where there is no value for the width, the applied algorithms were unable to calculate a reliable value.Calc_Length (excluding Access Ramps) – a generalized length of the feature calculated using spatial and mathematical algorithms on the feature. Lengths are rounded to the nearest whole number. In cases where there is no value for the length, the applied algorithms were unable to calculate a reliable value.Methodology: This dataset was digitized using a combination of G.I.S software, aerial imagery (2014 LARIAC), and a geographic dataset of property/right-of way lines.The general work flow is as follows:Create line work based on digital orthophotography, working from the face‐of‐curb (FOC) inward to the property right-of-way (ROW)Build sidewalk, parkway, driveway, and curb polygons from the digitized line workPopulate all polygons with the adjacent property PIN and classify all featuresCreate Curb Ramp pointsWarnings: This dataset has been provided to allow easy access and a visual display of Sidewalk and related features (Parkways, Driveway, Curb Ramps and Curbs). Every reasonable effort has been made to assure the accuracy of the data provided; nevertheless, some information may not be accurate. The City of Los Angeles assumes no responsibility arising from use of this information. THE MAPS AND ASSOCIATED DATA ARE PROVIDED WITHOUT WARRANTY OF ANY KIND, either expressed or implied, including but not limited to, the implied warranties of merchantability and fitness for a particular purpose. Other things to keep in mind about this dataset are listed below:Obscured Features – The existence of dense tree canopy or dark shadows in the aerial imagery tend to obscure or make it difficult to discern the extent of certain features, such as Driveways. In these cases, they may have been inferred from the path in the corresponding parcel. If a feature and approach was completely obscured, it was not digitized. In certain instances the coloring of the sidewalk and adjacent pavement rendered it impossible to identify the curb line or that a sidewalk existed. Therefore a sidewalk may or may not be shown where one actually may or may not exist.Context: The following links provide information on the policy context surrounding the creation of this dataset. It includes links to City of L.A. websites:Willits v. City of Los Angeles Class Action Lawsuit Settlementhttps://www.lamayor.org/willits-v-city-la-sidewalk-settlement-announcedSafe Sidewalks LA – program implemented to repair broken sidewalks in the City of L.A., partly in response to the above class action lawsuit settlementhttps://sidewalks.lacity.org/Data Source: Bureau of EngineeringNotes: Please be aware that this dataset is not actively being maintainedLast Updated: 5/20/20215/20/2021 - Added Calc_Width and Calc_Length fieldsRefresh Rate: One-time deliverable. Dataset not actively being maintained.

Data have been generated during the INFORE22 sea trial, run by CMRE from 21st February to 1st March 2022 in the Gulf of La Spezia to experiment and validate the CMRE hybrid robotic network in support of the INFORE maritime use case [*].

The dataset, which covers two days of experiments (28 February and 1 March 2022), includes:

•    GPS NMEA positions of targets
  

•    Target contact messages from marine autonomous robots
  

•    Thermal camera video streams with metadata on detected targets
  

•    AIS data (log of NMEA messages, processed messages, maps of vessel positions), showing vessel traffic in the Gulf of La Spezia during the experiments
  

This dataset, is complemented by a Raw acoustic data from SliTa array towed on AUVs (and Matlab reader) published by the authors in Zenodo https://zenodo.org/record/6375048.

For a full description of the dataset, see [**]

Conditions for use and distributions

This dataset is provided by NATO STO CMRE within the condition stated in the H2020 INFORE Grant and Consortium Agreement (GA. no. 825070) . The creation of derived products, as well the use in scientific publications must be pre-approved by CMRE and acknowledged.

Non liability clause

These data and software are provided in the scope of INFORE by NATO STO CMRE, in compliance with the INFORE open data strategy. Data and software are provided as they are. NATO and NATO STO CMRE decline any responsibility for bugs and any damage or accidental issue that the use of those data and software could cause.

References

[*] Gabriele Ferri, Raffaele Grasso, Elena Camossi, Francesca de Rosa, Alessandro Faggiani, Kevin LePage, Konstantina Bereta, Marios Vodas, Dimitris Kladis, Antonis Kontaxakis, Nikos Giatrakos, Antonios Deligiannakis, Maritime Use Case: Final Evaluation Report Work Package 3 Tasks 3.3 INFORE Deliverable D3.3

[**] Nikos Giatrakos, Antonios Deligiannakis, Arnau Montagud, Miguel Ponce de León, Thaleia Ntiniakou, Holger Arndt, Stefan Burkard, Konstantina Bereta, Marios Vodas, Dimitris Kladis, Raffaele Grasso, Gabriele Ferri, Arjan Vermeij, Alessandro Faggiani, Elena Camossi, Kevin Le Page: Data Management Plan V3 Work Package 8 Task 8.3 INFORE Deliverable 8.6

French-language art critics active between the mid-19th and 20th centuries (see http://critiquesdart.univ-paris1.fr/en). Each author’s page displays their primary and secondary bibliographies and identified archival sources. These documents are based on extensive research to produce primary bibliographies that can be considered to be comprehensive. For those writers whose complete works have been published, only their writing on art is listed. This online directory doubles as a database of primary bibliographies for the critics listed.

This dataset is neutral and non-discriminating, with no typology or classification other than the text format. It is multidisciplinary and makes no claim to assess the critical value of a text, preferring to provide as comprehensive an overview as possible of the authors’ literary output in whatever field (literature, art, politics, history, etc.) or medium (photography, film, fine arts, architecture, etc.) their interest encompassed.

Since these authors all wrote in French, they come from the artistic scenes in France, Belgium and Switzerland, not to mention the colonies of the period. Authors who wrote in more than one language are also included.

Our intention is to showcase research into art criticism, improve access to the documents and create links between researchers. The site will be gradually updated and enriched by the addition of further authors and greater detail for its current bibliographies.

Data set 1 : " database_journal_list.csv " description (en/fr) :

description of the fields:

"title": title of the journal

"ISSN": International identifier for serial publications

"temporal_cover": Start and end dates of publication

"city": place of publication, if known, otherwise NULL

descriptif des champs :

"titre": titre de la revue

"ISSN": Identifiant International des publications en série

"couverture_temporelle": Dates de début et de fin de publication

"ville": lieu de publication, si connu, sinon NULL

Dataset 2 : " Notices_critiques.csv " description (en/fr) : list of all the critics in the database (see http://critiquesdart.univ-paris1.fr/annuaire_critiques.php).

description of the fields:

"id": identifier in the database

"first name": First name of the author, if known, otherwise empty

"name": Author's last name

"birth": Year of birth

"death": date of death

"ISNI": International Standard Name Identifying people

descriptif des champs :

"id ": identifiant dans la base

"prenom ": Prénom de l'auteur, si connu, sinon vide

"nom": Nom de famille de l'auteur

"naissance": Année de naissance

"mort": date de mort

"ISNI": International Standard Name Identifier des personnes

Dataset 3 : "full_notices_dataset.csv" description (en/fr) : List of all the notices

description of the fields:

"critical_id": id of the review in the database

"title": title of the review

"subtitle": subtitle of the review, otherwise NULL

"type": type of criticism

"author_id": author's id in the database

"name": name of the author reviewer

"first name": first name of the author critic

"annee_ouvrage": year of publication if the review is a work or part of a work, otherwise NULL

"coordinator": potential coordinator of the structure, otherwise NULL

"titre_ouvrage": title of the work if the review is an article, otherwise NULL

"annee_periodique *": year of publication if the review is an article, otherwise NULL

"periodic_title": name of the journal if the review is an article, otherwise NULL

"city": place of publication, if known, otherwise NULL

descriptif des champs :

"id_critique": id de la critique dans la base

"titre": titre de la critique

"sous titre": sous titre de la critique, sinon NULL

"type": type de critique

"id_auteur": id de l'auteur dans la base

"nom": nom du critique auteur

"prenom": prénom du critique auteur

"annee_ouvrage": année de parution si la critique est un ouvrage ou partie d'un ouvrage, sinon NULL

"coordonateur": coordonateur potentiel de l'ouvrage, sinon NULL

"titre_ouvrage": titre de l'ouvrage si la critique est un article, sinon NULL

"annee_periodique *": année de parution si la critique est un article, sinon NULL

"titre_periodique": nom de la revue si la critique est un article, sinon NULL

"ville": lieu de publication, si connu, sinon NULL

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

This dataset is a synthesis of the number of production facilities in the “Hydraulic” sector per municipality of any level of voltage combined, in operation and in project. The data are derived from the aggregated dataset of the Register of Electricity and Gas Production Facilities that the DSOs make available to RTE for its unit publication in opendata. To trace a temporal evolution since 2017, data from the months in which all GRD/ELDs publish: namely, 31/12/2017 for 2017, 31/12/2018 for 2018, 31/12/2019 for 2019 and August 2020 for 2020 (to date).These elements can be viewed in the “Analysis” tab. A question about the dataset? A use case to share with other users? The Forum of open data experts electricity and gas is here for that! This dataset is a synthesis of the number of production facilities in the “Hydraulic” sector per municipality of any level of voltage combined, in operation and in project. The data are derived from the aggregated dataset of the Register of Electricity and Gas Production Facilities that the DSOs make available to RTE for its unit publication in opendata. To trace a temporal evolution since 2017, data from the months in which all GRD/ELDs publish: namely, 31/12/2017 for 2017, 31/12/2018 for 2018, 31/12/2019 for 2019 and August 2020 for 2020 (to date). These elements can be viewed in the “Analysis” tab. A question about the dataset? A use case to share with other users? The Forum of open data experts electricity and gas is here for that!

The global number of Youtube users in was forecast to continuously increase between 2024 and 2029 by in total 232.5 million users (+24.91 percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach 1.2 billion users and therefore a new peak in 2029. Notably, the number of Youtube users of was continuously increasing over the past years.User figures, shown here regarding the platform youtube, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Youtube users in countries like Africa and South America.