The City of Rochester and its staff use data about individuals in our community to inform decisions related to policies and programs we design, fund, and carry out. City staff must understand and be accountable to best practices and standards to guide the appropriate use of this information in an ethical and accurate manner that furthers the public good. With these disaggregated data standards, the City seeks to establish useful, uniform standards that guide City staff in their collection, stewardship, analysis, and reporting of information about individuals and their demographic characteristics.This internal guide provides recommended standards and practices to City of Rochester staff for the collection, analysis, and reporting of data related to following characteristics of an individual: Race & Ethnicity; Nativity & Citizenship Status; Language Spoken at Home & English Proficiency; Age; Sex, Gender, & Sexual Orientation; Marital Status; Disability; Address / Geography; Household Income & Size; Housing Tenure; Computer & Internet Use; Employment Status; Veteran Status; and Education Level. This kind of data that describes the characteristics of individuals in our community is disaggregated data. When we summarize data about these individuals and report the data at the group level, it becomes aggregated data. These disaggregated data standards can help City staff in different roles understand how to ask individuals about various demographic traits that may describe them, the collection of which may be useful to inform the City’s programs and policies. Note that this standards document does not mandate the collection of every one of these demographic factors for all analyses or program data intake designs – instead, it prompts City staff to intentionally design surveys and other data intake tools/applications to collect the right level of data to inform the City’s decision-making while also respecting the privacy of the individuals whose information the City seeks to gather. When a City team does choose to collect any of the above-mentioned demographic information about individuals in our community, we advise that they adhere to these standards.

The aggregated municipal government finance database (Aggregated MUNIFI) provides a selection of indicators on expenditure, revenue, and debt for the entire municipal government sector in OECD and EU countries. It is complemented by disaggregated data, that is data for each municipality in a given country, which can be downloaded directly from the links below.

The time coverage ranges from 2010 to 2022 (based on data availability). This database has been produced in the context of the OECD/EU joint project Strengthening analytical frameworks and data on subnational government finance and public employment with the goal of collecting, standardising, and disseminating high-quality comparable data for local government finance and employment.

This data provides information to assess the capacities and decision-making power of municipalities and regions, whose role is essential in developing place-based policy. It also allows for within and cross-country comparisons, which sheds light on the disparities between subnational governments in terms of fulfilling their mandates and their ability to raise own-source revenue. You will find more information on subnational government responsibilities in the World Observatory on Subnational Government Finance and Investment.

Abstract of associated article: We explore the effect of cross-sectional aggregation of data on estimation and test of asymmetric retail fuel price responses to wholesale price shocks. The analysis is performed on data collected daily from individual fuel stations in the Spanish metropolitan areas of Madrid and Barcelona. While the standard OLS estimator is applied to an error correction model in the case of the aggregated time series, we use the mean group approaches developed by Pesaran and Smith (1995) and Pesaran (2006) to estimate the short- and long-run micro-relations under heterogeneity. We found remarkable differences between the results of estimations using aggregated and disaggregated data, which are highly robust to both datasets considered. Our findings could help to explain many of the results in the literature on this research topic. On the one hand, they suggest that the typical estimation with aggregated data clearly tends to overestimate the persistence of shocks. On the other hand, we show that aggregation may generate a loss of efficiency in econometric estimates that is sufficiently large to hide the existence of the “rockets and feathers” phenomenon.

DUIA includes data on the socio-economic development and amenities of 86 cities from a total of 32 countries. DUIA is based on freely and easily available data sources and built on integration protocols and codes in R scripts, making both the construction of the database as a whole and specific statistical analyses fully transparent and replicable. DUIA is constructed in three steps. First, we draw upon remote sensing derived data from the Atlas of Urban Expansion to define city boundaries as accurately and consistently as possible across the different countries. Second, we draw upon survey data stored in IPUMS (Integrated Public Use Microdata Series) to include extensive, harmonized, and disaggregated data. Third, as we especially seek to contribute to comparative research outside the West, we developed tailor-made solutions to include Indian and Chinese cities for which data were not available in IPUMS.

The new scorecard tracks progress toward the World Bank Group's vision to create a world free of poverty on a livable planet. The Scorecard includes three types of indicators: - Vision indicators - reflect the new vision for the WBG, showing the WBG’s ambition and providing high-level measures to gauge the direction and pace of progress in tackling global challenges. Vision indicators contain aggregated and disaggregated development context data for all countries in the world, where data is available. The Scorecard reports the latest available global updates for each of these indicators. - Client context indicators - reflect the circumstances in client countries, including multidimensional aspects of poverty, and are aligned with the Sustainable Development Goals (SDGs). They serve to frame the challenges clients face, and the context in which the WBG operates. Client Context indicators contain aggregated and disaggregated development context data for World Bank client countries, based on country eligibility for financing and where data is available. The Scorecard also reports the latest available update for each of these indicators. - WBG Results indicators monitor WBG progress on some of the most critical global challenges. Results data include: - Active Portfolio Results: Contain achieved and expected results of WBG operations based on its active portfolio as of end of June 2024. Includes aggregated and disaggregated data. - Results achieved since July 1st, 2023: Contain cumulative results achieved between July 1st, 2023 - June 30, 2024 from active and closed projects. Results achieved before July 1st, 2023 are excluded from this calculation. Includes aggregated data for World Bank, IBRD and IDA only. IFC and MIGA do not currently report this data. - Operations Details: Operation-level detail is provided for World Bank projects. However, in alignment with IFC and MIGA Access to Information Policies, project-level data is available in an aggregated format on the WBG Scorecard, provided the minimum threshold to secure individual clients' data is satisfied. This collection includes only a subset of indicators from the source dataset.

This dataset is one of the outputs of the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) for 2010, which includes physical area, harvest area, production and yield, for 42 crops, disaggregated at the input-levels (e.g., irrigated/rainfed and high/low-input) on a 10 km grid globally. Production values in this dataset are given for each technology aggregated by categories - crops/food/non-food - with no information on individual crops. Unit of measure: Production for each technology: mt This new version of MapSPAM, available to download from the Harvard Dataverse Website, marks the third generation of the SPAM data series, following those of 2000 and 2005. More information on the production systems and selected crops is available in the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) full metadata at https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a

Election Data Attribute Field Definitions | Wisconsin Cities, Towns, & Villages Data Attributes Ward Data Overview: January 2025 municipal wards were collected in January 2025 by LTSB through LTSB's GeoData Collector. Current statutes require each county clerk, or board of election commissioners, no later than January 15 and July 15 of each year, to transmit to the LTSB, in an electronic format (approved by LTSB), a report confirming the boundaries of each municipality, ward and supervisory district within the county as of the preceding “snapshot” date of January 1 or July 1 respectively. Population totals for 2025 wards were estimated by aggregating 2020 US Census PL94-171 population data. LTSB has NOT topologically integrated the data. Election Data Overview: The 2024 Wisconsin election data that is included in this file was collected by LTSB from the *Wisconsin Elections Commission (WEC) after the general election. A disaggregation process was performed on this election data based on the municipal ward layer that was available at the time of the election. Disaggregation of Election Data: Election data is first disaggregated from reporting units to wards, and then to census blocks. Next, the election data is aggregated back up to wards, municipalities, and counties. The disaggregation of election data to census blocks is done based on total population. Detailed Methodology:Data is disaggregated first from reporting unit (i.e. multiple wards) to the ward level proportionate to the population of that ward.The data then is distributed down to the block level, again based on total population.When data is disaggregated to block or ward, we restrain vote totals not to exceed population 18 numbers, unless absolutely required.This methodology results in the following: Election data totals reported to the WEC at the state, county, municipal and reporting unit level should match the disaggregated election data total at the same levels. Election data totals reported to the WEC at ward level may not match the ward totals in the disaggregated election data file.Some wards may have more election data allocated than voter age population. This will occur if a change to the geography results in more voters than the 2020 historical population limits.Other things of note… We use a static, official ward layer (in this case created in 2025) to disaggregate election data to blocks. Using this ward layer creates some challenges. New wards are created every year due to annexations and incorporations. When these new wards are reported with election data, an issue arises wherein election data is being reported for wards that do not exist in our official ward layer. For example, if "Cityville" has four wards in the official ward layer, the election data may be reported for five wards, including a new ward from an annexation. There are two different scenarios and courses of action to these issues: When a single new ward is present in the election data but there is no ward geometry present in the official ward layer, the votes attributed to this new ward are distributed to all the other wards in the municipality based on population percentage. Distributing based on population percentage means that the proportion of the population of the municipality will receive that same proportion of votes from the new ward. In the example of Cityville explained above, the fifth ward may have five votes reported, but since there is no corresponding fifth ward in the official layer, these five votes will be assigned to each of the other wards in Cityville according the percentage of population.Another case is when a new ward is reported, but its votes are part of reporting unit. In this case, the votes for the new ward are assigned to the other wards in the reporting unit by population percentage; and not to wards in the municipality as a whole. For example, Cityville’s ward five was given as a reporting unit together with wards 1, 4, and 5. In this case, the votes in ward five are assigned to wards one and four according to population percentage. Outline Ward-by-Ward Election ResultsThe process of collecting election data and disaggregating to municipal wards occurs after a general election, so disaggregation has occurred with different ward layers and different population totals. We have outlined (to the best of our knowledge) what layer and population totals were used to produce these ward-by-ward election results.Election data disaggregates from WEC Reporting Unit -> Ward [Variant year outlined below]Elections 1990 – 2000: Wards 1991 (Census 1990 totals used for disaggregation)Elections 2002 – 2010: Wards 2001 (Census 2000 totals used for disaggregation)Elections 2012: Wards 2011 (Census 2010 totals used for disaggregation)Elections 2014 – 2016: Wards 2018 (Census 2010 totals used for disaggregation)Elections 2018: Wards 2018 (Census 2010 totals used for disaggregation)Elections 2020: Wards 2020 (Census 2020 totals used for disaggregation)Elections 2022: Wards 2022 (Census 2020 totals used for disaggregation)Elections 2024: Wards 2025 (Census 2020 totals used for disaggregation)Blocks -> Centroid geometry and spatially joined with Wards [All Versions]Each Block has an assignment to each of the ward versions outlined above.In the event that a ward exists now in which no block exists due to annexations, a block centroid was created with a population 0, and encoded with the proper Census IDs.Wards [All Versions] disaggregate -> Blocks This yields a block centroid layer that contains all elections from 1990 to 2024.Blocks [with all election data] -> Wards 2025 (then MCD 2025, and County 2025) All election data (including later elections) is aggregated to the Wards 2025 assignment of the blocks.Notes:Population of municipal wards 1991, 2001, 2011, 2020, 2022, and 2025 used for disaggregation were determined by their respective Census.Population and Election data will be contained within a county boundary. This means that even though MCD and ward boundaries vary greatly between versions of the wards, county boundaries have stayed the same, so data should total within a county the same between wards 2011 and wards 2025.Election data may be different for the same legislative district, for the same election, due to changes in the wards from 2011 and 2025. This is due to boundary corrections in the data from 2011 to 2025, and annexations, where a block may have been reassigned.*WEC replaced the previous Government Accountability Board (GAB) in 2016, which replaced the previous State Elections Board in 2008.

These data show the main gross labour market series by region and department: number of jobseekers at the end of the month, entries on the job centre lists, exits from the job centre lists, offers collected and satisfied. The data are raw and rounded to 100; there may therefore be slight discrepancies between the sum of disaggregated data and the aggregated series. The ages chosen for the different series are the end-of-month ages (age that the jobseeker will have at the end of the month).

These data relate to the number of jobseekers at the end of the month by category, sex and age group, by area of employment; they relate to the last month and their evolution over a year. They are raw and rounded to ten. There may therefore be slight discrepancies between the sum of disaggregated data and the aggregated series. Employment areas whose code starts with “00” are located in two regions. However, it is possible to count jobseekers separately from each regional part of these employment areas. These numbers of jobseekers in a regional part of an employment area straddling two regions are mentioned in italics. For each of the two tabs, the data are presented by region (including their respective regional part, in italics, if it shares an employment area with another region). Statistics are then presented for employment areas located in two regions and then for non-localised jobseekers in an employment area based on available source data (STMT). The municipality of residence of some jobseekers is not known. These jobseekers cannot therefore be located within an employment area. The corresponding staff are indicated at the bottom of the tables.

Modern slavery is a term that includes any form of human trafficking, slavery, servitude or forced labour, as set out in the Modern Slavery Act 2015. Potential victims of modern slavery in the UK that come to the attention of authorised ‘First Responder’ organisations are referred to the National Referral Mechanism (NRM).

Adults (aged 18 or above) must consent to being referred to the NRM, whilst children under the age of 18 need not consent to being referred. As specified in section 52 of the Modern Slavery Act 2015, public authorities in England and Wales have a statutory duty to notify the Home Office when they come across potential victims of modern slavery ('Duty to Notify' (DtN)). This duty is discharged by either referring a child or consenting adult potential victim into the NRM, or by notifying the Home Office via the DtN process if an adult victim does not consent to enter the NRM.

The Home Office publishes quarterly statistical bulletins and aggregated data breakdowns on the "https://www.gov.uk/government/collections/national-referral-mechanism-statistics" target="_blank"> National Referral Mechanism webpage on the GOV.UK website regarding the number of potential victims referred each quarter. To allow stakeholders and first responders more flexibility in analysing this data for their own strategic and operational planning, the disaggregated, pseudonymised dataset used to create the aggregated published data is also available from the UK Data Service as 'safeguarded' data. (The UKDS data are available in SPSS, Stata, tab-delimited text and CSV formats.)

Latest edition information

For the 17th edition (August 2025), the data file was amended to include Quarter 2 2025 cases, and the Data Notes documentation file was also updated.

This dataset is one of the outputs of the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) for 2010, which includes physical area, harvest area, production and yield, for 42 crops, disaggregated at the input-levels (e.g., irrigated/rainfed and high/low-input) on a 10 km grid globally. Harvested area values in this dataset are given for each technology aggregated by categories – crops/food/non-food - with no information on individual crops. Unit of measure: Harvested area for each technology: ha This new version of MapSPAM, available to download from the Harvard Dataverse Website, marks the third generation of the SPAM data series, following those of 2000 and 2005. More information on the production systems and selected crops is available in the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) full metadata at https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a

Users can view cross-nationally comparable data on the health workforce in the 193 WHO member states. Background The Global Atlas of the Health Workforce is a database maintained by the World Health Organization (WHO). This database allows users to view cross-nationally comparable data on the health workforce in the 193 WHO member states. Health workforce statistics includes the number or density of physicians, nurses, midwives, dentists, pharmacists, laboratory workers, community health workers, and public health workers. User Functionality Users can generate sta tistics pertaining to the health workforce. Users can view information by country, international region, or world, and choose a time period for which they are interested in viewing health workforce statistics. Aggregated and disaggregated data are available. In addition, users can view regional summaries of the health workforce. Data Notes The Global Atlas of the Health Workforce is updated periodically. Data are available for 1995-2011. Data are derived from national population censuses, labor force and employment surveys, health facility assessments, and official country reports to the WHO. Regional and country summaries are available.

The National Pollutant Release Inventory (NPRI) is Canada's public inventory of pollutant releases (to air, water and land), disposals and transfers for recycling.

These files contain NPRI release data for the past five years in CSV format, aggregated by Province, Industry Type and Substance, and disaggregated by media (air, water and land). The number of reporting facilities represented by each aggregated data point is included*. The results can be further broken down using the pre-defined queries available in the online NPRI Facility Search.

There are important factors that should be considered prior to the use and interpretation of NPRI data. Additional information is available on the NPRI web site.

*The number of facilities returned by the online NPRI data search may differ from the number contained in the download files. The online results take into account the facility’s reported releases, disposals and transfers, but do not distinguish between media. They also include facilities reporting only under Ontario Regulation 127/01 and facilities submitting “did not meet criteria” reports.

More NPRI datasets and mapping products are available here: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/access.html

The analysis of census data aggregated by administrative units introduces a statistical bias known as the modifiable areal unit problem (MAUP). Previous researches have mostly assessed the effect of MAUP on upscaling models. The present study contributes to clarify the effects of MAUP on the downscaling methodologies, highlighting how a priori choices of scales and shapes could influence the results. We aggregated chicken and duck fine-resolution census in Thailand, using three administrative census levels in regular and irregular shapes. We then disaggregated the data within the Gridded Livestock of the World analytical framework, sampling predictors in two different ways. A sensitivity analysis on Pearson’s r correlation statistics and RMSE was carried out to understand how size and shapes of the response variables affect the goodness-of-fit and downscaling performances. We showed that scale, rather than shapes and sampling methods, affected downscaling precision, suggesting that training the model using the finest administrative level available is preferable. Moreover, datasets showing non-homogeneous distribution but instead spatial clustering seemed less affected by MAUP, yielding higher Pearson’s r values and lower RMSE compared to a more spatially homogenous dataset. Implementing aggregation sensitivity analysis in spatial studies could help to interpret complex results and disseminate robust products.

The Armed Conflict Location & Event Data Project (ACLED) is a US-registered non-profit whose mission is to provide the highest quality real-time data on political violence and demonstrations globally. The information collected includes the type of event, its date, the location, the actors involved, a brief narrative summary, and any reported fatalities. ACLED users rely on our robust global dataset to support decision-making around policy and programming, accurately analyze political and country risk, support operational security planning, and improve supply chain management.ACLED’s transparent methodology, expert team composed of 250 individuals speaking more than 70 languages, real-time coding system, and weekly update schedule are unrivaled in the field of data collection on conflict and disorder. Global Coverage: We track political violence, demonstrations, and strategic developments around the world, covering more than 240 countries and territories.Published Weekly: Our data are collected in real time and published weekly. It is the only dataset of its kind to provide such a high update frequency, with peer datasets most often updating monthly or yearly.Historical Data: Our dataset contains at least two full years of data for all countries and territories, with more extensive coverage available for multiple regions.Experienced Researchers: Our data are coded by experienced researchers with local, country, and regional expertise and language skills.Thorough Data Collection and Sourcing: Pulling from traditional media, reports, local partner data, and verified new media, ACLED uses a tailor-made sourcing methodology for individual regions/countries.Extensive Review Process: Our data go through an exhaustive multi-stage quality assurance process to ensure their accuracy and reliability. This process includes both manual and automated error checking and contextual review.Clean, Standardized, and Validated: Our data can be easily connected with internal dashboards through our API or downloaded through the Data Export Tool on our website.Resources Available on ESRI’s Living AtlasACLED data are available through the Living Atlas for the most recent 12 month period. The data are mapped to the centroid of first administrative divisions (“admin1”) within countries (e.g., states, districts, provinces) and aggregated by month. Variables in the data include:The number of events per admin1-month, disaggregated by event type (protests, riots, battles, violence against civilians, explosions/remote violence, and strategic developments)A conservative estimate of reported fatalities per admin1-monthThe total number of distinct violent actors active in the corresponding admin1 for each monthThis Living Atlas item is a Web Map, which provides a pre-configured view of ACLED event data in a few layers:ACLED Event Counts layer: events per admin1-month, styled by predominant event type for each location.ACLED Violent Actors layer: the number of distinct violent actors per admin1-month.ACLED Fatality Estimates layer: the estimated number of fatalities from political violence per admin1-month.These layers are based on the ACLED Conflict and Demonstrations Event Data Feature Layer, which has the same data but only a basic default styling that is similar to the Event Counts layer. The Web Map layers are configured with a time-slider component to account for the multiple months of data per admin1 unit. These indicators are also available in the ACLED Conflict and Demonstrations Data Key Indicators Group Layer, which includes the same preconfigured layers but without the time-slider component or background layers.Resources Available on the ACLED WebsiteThe fully disaggregated dataset is available for download on ACLED's website including:Date (day, month, year)Actors, associated actors, and actor typesLocation information (ADMIN1, ADMIN2, ADMIN3, location and geo coordinates)A conservative fatality estimateDisorder type, event types, and sub-event typesTags further categorizing the data A notes column providing a narrative of the event For more information, please see the ACLED Codebook.To explore ACLED’s full dataset, please register on the ACLED Access Portal, following the instructions available in this Access Guide. Upon registration, you’ll receive access to ACLED data on a limited basis. Commercial users have access to 3 free data downloads company-wide with access to up to one year of historical data. Public sector users have access to 6 downloads of up to three years of historical data organization-wide. To explore options for extended access, please reach out to our Access Team (access@acleddata.com).With an ACLED license, users can also leverage ACLED’s interactive Global Dashboard and check in for weekly data updates and analysis tracking key political violence and protest trends around the world. ACLED also has several analytical tools available such as our Early Warning Dashboard, Conflict Alert System (CAST), and Conflict Index Dashboard.

Heat causes protein misfolding and aggregation and in eukaryotic cells triggers aggregation of proteins and RNA into stress granules. We have carried out extensive proteomic studies to quantify heat-triggered aggregation and subsequent disaggregation in budding yeast, identifying >170 endogenous proteins aggregating within minutes of heat shock in multiple subcellular compartments. We demonstrate that these aggregated proteins are not misfolded and destined for degradation. Stable-isotope labeling reveals that even severely aggregated endogenous proteins are disaggregated without degradation during recovery from shock, contrasting with the rapid degradation observed for exogenous thermolabile proteins. Although aggregation likely inactivates many cellular proteins, in the case of a heterotrimeric aminoacyl-tRNA synthetase complex, the aggregated proteins remain active with unaltered fidelity. We propose that most heat-induced aggregation of mature proteins reflects the operation of an adaptive, autoregulatory process of functionally significant aggregate assembly and disassembly that aids cellular adaptation to thermal stress.

This data set currently features 13 semesters of data from a required junior level political theory course at a midsize southern public university. These were graded using an adapted version of the WSU Critical Thinking Rubric and features aggregated and disaggregated measures of critical thinking as well individual and course level sources of variation. n=735

The erythrocyte sedimentation rate (ESR) test has been used for over a century. The Westergren method is routinely used in a variety of clinics. However, the mechanism of erythrocyte sedimentation remains unclear, and the 60 min required for the test seems excessive. We investigated the effects of cell aggregation during blood sedimentation and electrical conductivity at different hematocrits. A sample of blood was drop cast into a small chamber with two planar electrodes placed on the bottom. The measured blood conductivity increased slightly during the first minute and decreased thereafter. We explored various methods of enhancing or retarding the erythrocyte aggregation. Using experimental measurements and theoretical calculations, we show that the initial increase in blood conductivity was indeed caused by aggregation, while the subsequent decrease in conductivity resulted from the deposition of erythrocytes. We present a method for calculating blood conductivity based on effective medium theory. Erythrocytes are modeled as conducting spheroids surrounded by a thin insulating membrane. A digital camera was used to investigate the erythrocyte sedimentation behavior and the distribution of the cell volume fraction in a capillary tube. Experimental observations and theoretical estimations of the settling velocity are provided. We experimentally demonstrate that the disaggregated cells settle much slower than the aggregated cells. We show that our method of measuring the electrical conductivity credibly reflected the ESR. The method was very sensitive to the initial stage of aggregation and sedimentation, while the sedimentation curve for the Westergren ESR test has a very mild slope in the initial time. We tested our method for rapid estimation of the Westergren ESR. We show a correlation between our method of measuring changes in blood conductivity and standard Westergren ESR method. In the future, our method could be examined as a potential means of accelerating ESR tests in clinical practice.

Data These data relate to jobseekers registered on average during the quarter at Pôle emploi in categories A, B, C by sex, by age group and by municipality (based on the Official Geographic Code, as of 1 January 2022), for the 4th quarters over a rolling 10-year period. They are raw and rounded to a multiple of 5. There may therefore be slight differences between the sum of the disaggregated data and the aggregated series. Annual developments should be taken with caution: on small municipalities, the effect of rounding can be significant and the annual evolution is then very impacted. For example, a municipality that sees an increase in the number of jobseekers from 17 to 18 (an increase of 6 %) will have staff numbers rounded up to 15 and 20, i.e. an increase of 33 %. The ages used for the different series are the ages at the end of the month (age that the job seeker will have at the end of the month in question). For each of the communes, the region and the department to which they belong are specified. ### Definition Full documentation on data on registered jobseekers and vacancies collected by France Travail can be found on the Dares website (see document Methodological documentation - Jobseekers). Information on the Official Geographic Code is available on the INSEE website. ### Field * the geographical grouping ‘**** Metropolitan France’ includes all the French territories on the European continent (96 departments); * The geographical grouping ‘France’ includes metropolitan France and the overseas departments/regions (DROM), with the exception of Mayotte. ### Source The data are taken from the files of the Monthly Labour Market Statistics (STMT) of Dares and France Travail. ### Warnings In addition to labour market developments, data on jobseekers can be affected by a number of factors: changes to the rules on compensation or support for jobseekers, procedural changes, incidents. A document presents the main procedural changes and incidents affecting the statistics of jobseekers since 2011. The municipalities of Sannerville (14666) and Troarn (14712) were merged into the new municipality of Saline (14712) from 2017 to 2019, and were then re-established on 1 January 2020. The information for these two municipalities over this period should therefore be considered with caution.

The Registered Apprenticeship data displayed in this resource is derived from several different sources with differing abilities to provide disaggregated data. The 25 federally-administered states and 16 federally-recognized State Apprenticeship Agencies (SAAs) use the Employment and Training Administration's Registered Apprenticeship Partners Information Database System (RAPIDS) to provide individual apprentice and sponsor data. This subset of data is referred to as RAPIDS data and can be disaggregated to provide additional specificity. The federal subset of that data (25 states plus national programs) is known as the Federal Workload. The remaining federally recognized SAAs and the U.S. Military Apprenticeship Program (USMAP) provide limited aggregate data on a quarterly basis that is then combined with RAPIDS data to provide a national data set on high-level metrics (apprentices and programs) but cannot generally be broken out in greater detail beyond the data provided here.