The General Data Protection Regulation (GDPR) provides, since 25 May 2018, for the mandatory designation of a Data Protection Officer (DPO) in public services and, under certain conditions, by companies and associations.

The delegate — also known as the Data Protection Officer (DPO) — is responsible for ensuring GDPR compliance with the processing of personal data of the body that designated him or her. Internal or external, the delegate may also be appointed on behalf of several bodies.

To ensure the effectiveness of his/her tasks, the delegate shall:

— must have specific professional qualities and knowledge; — must benefit from material and organisational resources, resources and positioning enabling it to carry out its tasks effectively and independently.

To learn more about the role of delegate: https://www.cnil.fr/fr/devenir-delegue-la-protection-des-donnees.

In accordance with the applicable texts, the CNIL shall publish in an open and easily reusable format the name and professional contact details of the bodies that have appointed a Data Protection Officer, as well as the means of contacting the Data Protection Officer.

** Warning 1:** The published data, including the public contact details of delegates, are extracted from the designations of delegates as received by the CNIL via its dedicated teleservice. Any delegate may request the modification of the contact details published directly to the CNIL’s Data Protection Officers Service.

** Warning 2:** Any re-use of published data which would have the nature of personal data (telephone number, e-mail address, etc.) presupposes, on the part of the re-user, verification of the full fulfilment of his/her obligations under the GDPR, in particular in terms of informing the delegates concerned and respecting their other rights as defined by the European Regulation. Otherwise, the re-user would in particular be exposed to the penalties provided for in the GDPR.

This Dataset indicates average salary by position title and grade for full-time regular employees. Data excludes elected, appointed, non-merit and temporary employees. Underfilled positions are also excluded from the dataset. Update Frequency : Annually

This shows the monthly estimated number of attended appointments by appointment category (mode of consultation, type of practitioner and reason for appointment), measure (number of attended appointments or average number of attended appointments per working day) and area (health board and cluster).

Though the defense ministry has been a bastion of male power, a growing number of states have appointed women to this portfolio. What explains men’s dominance over these positions? Which factors predict women’s appointments? With comprehensive cross-national data from the post-Cold War era, we develop and test three sets of hypotheses concerning women’s access to the defense ministry. We show that women remain excluded when the portfolio’s remit reinforces traditional beliefs about the masculinity of the position, particularly in states that are engaged in fatal disputes, governed by military dictators, and large military spenders. By contrast, female defense ministers emerge when expectations about women’s role in politics have changed—i.e., in states with female chief executives and parliamentarians. Women are also first appointed to the post when its meaning diverges from traditional conceptions of the portfolio, particularly in countries concerned with peacekeeping and in former military states with left-wing governments.

The aim of the publication is to inform users about activity and usage of GP appointments historically and how primary care is impacted by seasonal pressures, such as winter. NHS England publishes this information to support winter preparedness and provide information about some activity within primary care. The publication covers historic appointments, marked as attended or did not attend, from national to practice level coverage. The aim is to inform users, who range from a healthcare professional to an inquiring citizen, about appointments within primary care. The publication includes data from participating practices and Primary Care Networks (PCNs) using EMIS, TPP, Informatica, Cegedim (previously Vision) and Babylon (GP at Hand) GP systems. NHS England produce this information monthly, containing information about the most recent 30 months. The publication includes important information, however it does not show the totality of GP activity/workload. The data presented only contains information which was captured on the GP practice and PCN appointment systems. This limits the activity reported on and does not represent all work happening within a primary care setting or assess the complexity of activity. No patient identifiable information has been collected or is included in this release. Between December 2020 and present the data contained in this publication will no longer contain covid-19 vaccination activity collected from GP System Suppliers as part of the General Practice Appointments Data. These appointments have been removed using the methodology outlined in the supporting information. In order to gain a more complete picture of general practice activity we will publish covid-19 vaccination activity carried out by PCN’s or GP Practice’s from the NIMS (National Immunisation Management Service) vaccination dataset. This publication now includes statistics on the duration of appointments, SDS role and the recorded national category, service setting and context type of the appointment. Both HCP Type and SDS role are currently presented for comparison purposes, but moving forward the intention is to only publish SDS Role Groups and remove HCP Type. Further information can be found in the supporting guidance below. Appointments recorded in Primary Care Network (PCN) appointment systems are included within this publication at national level from June 2023.

This dataset represents the spatial boundaries of clean water supply served by United Utilities. This dataset is critical for understanding the division of service areas among different companies within the United Kingdom.  CaveatsThis shapefile is intended solely for geospatial analysis. The authoritative legal delineation of areas is maintained in the maps and additional details specified in the official appointments of companies as water and/or sewerage undertakers, along with any alterations to their areas.The shapefile does not encompass data on any structures or properties that, despite being outside the designated boundary,are included in the area, or those within the boundary yet excluded from the area.In terms of geospatial analysis and visual representation, the mean high-water line has been utilised to define any boundaryextending into the sea, though it's more probable that the actual boundary aligns with the low water mark. Furthermore,islands that are incorporated into the area might not be included in this representation.The data was inherited as part of the original instrument of appointment, signed by the Secretary of State. The shapefile provides a geospatial representation of this data taken from the original paper maps.The position of the boundary is approximate only and is given in accordance with the best information currently available. United Utilities Water will not accept liability for any loss or damage caused by the actual position being different from thatshown.

Increase the number of women on all State Government boards and committees to 50% on average by 2014, and maintain thereafter by ensuring that 50% of women are appointed, on average, each quarter.

Invited talk given by Tim Evans (Imperial College London) at the EPSRC Workshop on "Scaling in Social Systems” held at the Saïd Business School, Oxford on 1st December 2011. Abstract:

The pattern of innovation seen through citations of academic papers has long fascinated academics. It has been known for at least fifty years that the data shows various long tailed distributions. In this talk I will look at some of the features of the data and show how to extract some simple universal patterns. I will discuss some of the implications of the results and some of the further questions it raises. •What is a citation? •What does an individual citation mean? •Is the data perfect? •Why citation count? •If not citation count, what else? •What does this data say about me? •Why h-index? •What is a self-citation? •How else can I use this data? •How will things change?

Tim S. Evans – Mini Biography Tim studied the mixture of quantum field theory and statistical physics in his PhD at Imperial College London. He was supervised by Prof. Ray Rivers who also supervised another speaker, Prof. Luis Bettencourt. Tim then spent time as a researcher at the University of Alberta in Edmonton Canada, before returning to research positions back here at Imperial, latterly as a Royal Society University Research Fellow. He was appointed to a lectureship at Imperial in 1997. Around 2003 he expanded his work on statistical physics to cover at problems in complexity, with a particular interest in network methods. This has included participation in an EU collaboration with social scientists on innovation, ―ISCOM, run in part by Prof. Geoff West (another speaker today). This fuelled his interest in social science applications and started an on going collaboration with an archaeologist.

State and Local Public Health Departments in the United States Governmental public health departments are responsible for creating and maintaining conditions that keep people healthy. A local health department may be locally governed, part of a region or district, be an office or an administrative unit of the state health department, or a hybrid of these. Furthermore, each community has a unique "public health system" comprising individuals and public and private entities that are engaged in activities that affect the public's health. (Excerpted from the Operational Definition of a functional local health department, National Association of County and City Health Officials, November 2005) Please reference http://www.naccho.org/topics/infrastructure/accreditation/upload/OperationalDefinitionBrochure-2.pdf for more information. Facilities involved in direct patient care are intended to be excluded from this dataset; however, some of the entities represented in this dataset serve as both administrative and clinical locations. This dataset only includes the headquarters of Public Health Departments, not their satellite offices. Some health departments encompass multiple counties; therefore, not every county will be represented by an individual record. Also, some areas will appear to have over representation depending on the structure of the health departments in that particular region. Town health officers are included in Vermont and boards of health are included in Massachusetts. Both of these types of entities are elected or appointed to a term of office during which they make and enforce policies and regulations related to the protection of public health. Visiting nurses are represented in this dataset if they are contracted through the local government to fulfill the duties and responsibilities of the local health organization. Since many town health officers in Vermont work out of their personal homes, TechniGraphics represented these entities at the town hall. This is denoted in the [DIRECTIONS] field. Effort was made by TechniGraphics to verify whether or not each health department tracks statistics on communicable diseases. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard HSIP fields populated by TechniGraphics. Double spaces were replaced by single spaces in these same fields. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based on this field, the oldest record dates from 11/18/2009 and the newest record dates from 01/08/2010.

Russia Average Monthly Household Income per Capita: Quarterly data was reported at 29,920.000 RUB in Mar 2019. This records a decrease from the previous number of 37,492.710 RUB for Dec 2018. Russia Average Monthly Household Income per Capita: Quarterly data is updated quarterly, averaging 16,146.400 RUB from Mar 1999 (Median) to Mar 2019, with 81 observations. The data reached an all-time high of 37,492.710 RUB in Dec 2018 and a record low of 1,302.800 RUB in Mar 1999. Russia Average Monthly Household Income per Capita: Quarterly data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HA008: Household Income per Capita. Taking into account the lump sum monetary payment in January 2017 of 5 thousand roubles appointed according to the Federal law of November 22, 2016 № 385-FZ (EP-2017). С учетом единовременной денежной выплаты в январе 2017г. в размере 5 тысяч рублей в соответствии с Федеральным законом от 22 ноября 2016г. № 385-ФЗ (далее ЕВ-2017).

Because collecting precise and accurate chemistry data is often challenging, chemistry data sets usually only span a small region of chemical space, which limits the performance and the scope of applicability of data-driven models. To address this issue, we integrated an active learning machine with automatic ab initio calculations to form a self-evolving model that can continuously adapt to new species appointed by the users. In the present work, we demonstrate the self-evolving concept by modeling the formation enthalpies of stable closed-shell polycyclic species calculated at the B3LYP/6-31G(2df,p) level of theory. By combining a molecular graph convolutional neural network with a dropout training strategy, the model we developed can predict density functional theory (DFT) enthalpies for a broad range of polycyclic species and assess the quality of each predicted value. For the species which the current model is uncertain about, the automatic ab initio calculations provide additional training data to improve the performance of the model. For a test set composed of 2858 cyclic and polycyclic hydrocarbons and oxygenates, the enthalpies predicted by the model agree with the reference DFT values with a root-mean-square error of 2.62 kcal/mol. We found that a model originally trained on hydrocarbons and oxygenates can broaden its prediction coverage to nitrogen-containing species via an active learning process, suggesting that the continuous learning strategy is not only able to improve the model accuracy but is also capable of expanding the predictive capacity of a model to unseen species domains.

Russia Average Monthly Pension: Nominal: Annual data was reported at 13,360.200 RUB in 2018. This records an increase from the previous number of 13,303.700 RUB for 2017. Russia Average Monthly Pension: Nominal: Annual data is updated yearly, averaging 2,364.000 RUB from Dec 1992 (Median) to 2018, with 27 observations. The data reached an all-time high of 13,360.200 RUB in 2018 and a record low of 1.561 RUB in 1992. Russia Average Monthly Pension: Nominal: Annual data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GC025: Nominal and Real Pension. Taking into account the lump sum monetary payment in January 2017 of 5 thousand roubles appointed according to the Federal law of November 22, 2016 № 385-FZ (EP-2017). С учетом единовременной денежной выплаты в январе 2017г. в размере 5 тысяч рублей в соответствии с Федеральным законом от 22 ноября 2016г. № 385-ФЗ (далее ЕВ-2017).

Children in DFPS custody are those for whom a court has appointed DFPS legal responsibility through temporary or permanent managing conservatorship or other court ordered legal basis. This chart includes any child in DFPS custody on August 31 of the fiscal year. A description of the different types of legal statuses is in the CPS glossary: https://www.dfps.texas.gov/About_DFPS/Data_Book/Child_Protective_Services/Resources/glossary.asp Visit dfps.texas.gov for information on Children In Legal Responsibility and all DFPS programs.

This data calculates the average time a transaction and wait times per customer occurs with processing time measured in minutes. The customer transactions are by appointment only. It is updated by the 15th every month with full report on previous month's data.

Supplementary Data associated with the manuscript "A Cheminformatics Workflow for Higher-throughput Modeling of Chemical Exposures from Biosolids".

bioRxiv preprint (not peer reviewed): https://doi.org/10.1101/2025.04.03.647109

BST_BulkUpload.mdb: Microsoft Access implementation of the Biosolids Screening Tool, in a version that has the capability for bulk-uploading chemicals.

BST_BulkChemicalImportTool.xlsm: Microsoft Excel workbook, providing the template used by autoBST to produce output suitable for bulk upload into the BST.

autobst.zip: A ZIP archive of the autoBST code and data files, also available on GitHub at https://github.com/USEPA/CompTox-ExpoCast-autoBST .

Supplementary Table 1: AutoBST workflow output (Excel workbook of BST parameters suitable for uploading using the Bulk Upload Tool) when biosolids concentrations are set to National Sewage Sludge Survey mean concentrations.

Supplementary Table 2: AutoBST workflow output (Excel workbook of BST parameters suitable for uploading using the Bulk Upload Tool when biosolids concentrations are set to National Sewage Sludge Survey 95th percentile concentrations.

Supplementary Table 3: AutoBST workflow output (Excel workbook of BST parameters suitable for uploading using the Bulk Upload Tool) when biosolids concentrations are set to 1 ppb for all chemicals.

Supplementary Table 4: BST simulation results when biosolids concentrations are set to National Sewage Sludge Survey mean concentrations (i.e., when Supplementary Table 1 is used as input). See BST User Guide for details.

Supplementary Table 5: BST simulation results when biosolids concentrations are set to National Sewage Sludge Survey 95th percentile concentrations (i.e., when Supplementary Table 2 is used as input). See BST User Guide for details.

Supplementary Table 6: BST simulation results when biosolids concentrations are set to 1 ppb for all chemicals (i.e., when Supplementary Table 3 is used as input). See BST User Guide for details.

Supplementary Table 7: Chemical rankings by BST-predicted oral average daily dose (ADD) for an adult farmer, under each of the three land-application use (LAU) scenarios (Crop, Pasture, and Reclamation), for each biosolids concentration scenario considered (NSSS mean, NSSS 95th percentile, and 1 ppb).

Disclaimer: This data set reflects the opinions of the authors and does not necessarily represent U.S. EPA policy. This research was supported in part by an appointment to the U.S. Environmental Protection Agency (EPA) Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and the U.S. Environmental Protection Agency. ORISE is managed by Oak Ridge Associated Universities (ORAU) under DOE contract number DE-SC0014664. All opinions expressed are those of the authors and do not necessarily reflect the policies and views of US EPA, DOE, or ORAU/ORISE. These data and the associated manuscript have been reviewed by the Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, and approved for publication. Approval does not signify that the contents reflect the views of the Agency. Any mention of trade names, products, or services does not imply an endorsement by the U.S. government or the EPA. EPA does not endorse any commercial products, services, or enterprises.

Active Article III Federal JudgesThis feature layer, utilizing data from the Federal Judicial Center (FJC), displays active federal judges, as defined by Article III of the U.S. Constitution. Per US Courts, "Article III states that these judges 'hold their office during good behavior,' which means they have a lifetime appointment, except under very limited circumstances. Article III judges can be removed from office only through impeachment by the House of Representatives and conviction by the Senate."Federal judges listed include those serving in the following court systems.Supreme CourtCourts of AppealsDistrict CourtsCourt of International TradeNote #1: In situations where a court has multiple courthouses, one location was chosen as a representative for the court.Note #2: This layer contains information on appointed judges who have died, are now in "senior" status or are not currently Article III federal judges for other reasons. Those judges are not included in this portrayal.John Glover RobertsChief Justice - Supreme Court of the United StatesData currency: Current (updated daily)Data downloaded from: Biographical Directory of Article III Federal Judges: Export > Federal Judicial Service (CSV file)Data modification: The following fields were added to the original data: Address, City, State, Zip Code, Source, Representative court location, Latitude and Longitude.For more information: Biographical Directory of Article III Federal Judges, 1789-present and About Federal JudgesFor feedback, please contact: ArcGIScomNationalMaps@esri.comUS CourtsPer U.S. Courts, "The U.S. Courts were created under Article III of the Constitution to administer justice fairly and impartially, within the jurisdiction established by the Constitution and Congress."

Average Monthly Pension: Nominal data was reported at 13,396.300 RUB in Nov 2018. This records an increase from the previous number of 13,383.400 RUB for Oct 2018. Average Monthly Pension: Nominal data is updated monthly, averaging 2,841.600 RUB from Jan 1995 (Median) to Nov 2018, with 287 observations. The data reached an all-time high of 17,425.600 RUB in Jan 2017 and a record low of 120.300 RUB in Jan 1995. Average Monthly Pension: Nominal data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GC025: Nominal and Real Pension. Taking into account the lump sum monetary payment of 5 thou. rubles in January 2017 appointed according to the Federal law of November 22, 2016. С учетом единовременной денежной выплаты в январе 2017г. в размере 5 тысяч рублей, назначенной в соответствии с Федеральным законом от 22 ноября 2016г. № 385-ФЗ.

Russia Average Monthly Pension: Nominal: Prev Month=100 data was reported at 100.100 Prev Mth=100 in Nov 2018. This records an increase from the previous number of 100.000 Prev Mth=100 for Oct 2018. Russia Average Monthly Pension: Nominal: Prev Month=100 data is updated monthly, averaging 100.100 Prev Mth=100 from Jan 1995 (Median) to Nov 2018, with 287 observations. The data reached an all-time high of 140.200 Prev Mth=100 in Jan 2017 and a record low of 74.000 Prev Mth=100 in Feb 2017. Russia Average Monthly Pension: Nominal: Prev Month=100 data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GC025: Nominal and Real Pension. Taking into account the lump sum monetary payment of 5 thou. rubles in January 2017 appointed according to the Federal law of November 22, 2016. С учетом единовременной денежной выплаты в январе 2017г. в размере 5 тысяч рублей, назначенной в соответствии с Федеральным законом от 22 ноября 2016г. № 385-ФЗ.

A List Certification includes the names of eligible candidates on an Active Civil Service List that has been established. The Certification may contain part of a list, the whole list, or multiple lists at the request of an appointing agency, to fill vacancies and/or replace provisionals. Eligible candidates on a Certification may be considered for probable appointment at the appointing Agency. For more information visit DCAS’ “Work for the City” webpage at: https://www1.nyc.gov/site/dcas/employment/take-an-exam.page