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CDC increases the health security of our nation. As the nation’s health protection agency, CDC saves lives and protects people from health threats. To accomplish our mission, CDC conducts critical science and provides health information that protects our nation against expensive and dangerous health threats, and responds when these arise.

On August 25th, 2022, Metro Council Passed Open Data Ordinance; previously open data reports were published on Mayor Fischer's Executive Order, You can find here both the Open Data Ordinance, 2022 (PDF) and the Mayor's Open Data Executive Order, 2013 Open Data Annual ReportsPage 6 of the Open Data Ordinance, Within one year of the effective date of this Ordinance, and thereafter no later than September1 of each year, the Open Data Management Team shall submit to the Mayor and Metro Council an annual Open Data Report.The Open Data Management team (also known as the Data Governance Team is currently led by the city's Data Officer Andrew McKinney in the Office of Civic Innovation and Technology. Previously, it was led by the former Data Officer, Michael Schnuerle and prior to that by Director of IT.Open Data Ordinance O-243-22 TextLouisville Metro GovernmentLegislation TextFile #: O-243-22, Version: 3ORDINANCE NO._, SERIES 2022AN ORDINANCE CREATING A NEW CHAPTER OF THE LOUISVILLE/JEFFERSONCOUNTY METRO CODE OF ORDINANCES CREATING AN OPEN DATA POLICYAND REVIEW. (AMENDMENT BY SUBSTITUTION)(AS AMENDED).SPONSORED BY: COUNCIL MEMBERS ARTHUR, WINKLER, CHAMBERS ARMSTRONG,PIAGENTINI, DORSEY, AND PRESIDENT JAMESWHEREAS, Metro Government is the catalyst for creating a world-class city that provides itscitizens with safe and vibrant neighborhoods, great jobs, a strong system of education and innovationand a high quality of life;WHEREAS, it should be easy to do business with Metro Government. Online governmentinteractions mean more convenient services for citizens and businesses and online governmentinteractions improve the cost effectiveness and accuracy of government operations;WHEREAS, an open government also makes certain that every aspect of the builtenvironment also has reliable digital descriptions available to citizens and entrepreneurs for deepengagement mediated by smart devices;WHEREAS, every citizen has the right to prompt, efficient service from Metro Government;WHEREAS, the adoption of open standards improves transparency, access to publicinformation and improved coordination and efficiencies among Departments and partnerorganizations across the public, non-profit and private sectors;WHEREAS, by publishing structured standardized data in machine readable formats, MetroGovernment seeks to encourage the local technology community to develop software applicationsand tools to display, organize, analyze, and share public record data in new and innovative ways;WHEREAS, Metro Government’s ability to review data and datasets will facilitate a betterUnderstanding of the obstacles the city faces with regard to equity;WHEREAS, Metro Government’s understanding of inequities, through data and datasets, willassist in creating better policies to tackle inequities in the city;WHEREAS, through this Ordinance, Metro Government desires to maintain its continuousimprovement in open data and transparency that it initiated via Mayoral Executive Order No. 1,Series 2013;WHEREAS, Metro Government’s open data work has repeatedly been recognized asevidenced by its achieving What Works Cities Silver (2018), Gold (2019), and Platinum (2020)certifications. What Works Cities recognizes and celebrates local governments for their exceptionaluse of data to inform policy and funding decisions, improve services, create operational efficiencies,and engage residents. The Certification program assesses cities on their data-driven decisionmakingpractices, such as whether they are using data to set goals and track progress, allocatefunding, evaluate the effectiveness of programs, and achieve desired outcomes. These datainformedstrategies enable Certified Cities to be more resilient, respond in crisis situations, increaseeconomic mobility, protect public health, and increase resident satisfaction; andWHEREAS, in commitment to the spirit of Open Government, Metro Government will considerpublic information to be open by default and will proactively publish data and data containinginformation, consistent with the Kentucky Open Meetings and Open Records Act.NOW, THEREFORE, BE IT ORDAINED BY THE COUNCIL OF THELOUISVILLE/JEFFERSON COUNTY METRO GOVERNMENT AS FOLLOWS:SECTION I: A new chapter of the Louisville Metro Code of Ordinances (“LMCO”) mandatingan Open Data Policy and review process is hereby created as follows:§ XXX.01 DEFINITIONS. For the purpose of this Chapter, the following definitions shall apply unlessthe context clearly indicates or requires a different meaning.OPEN DATA. Any public record as defined by the Kentucky Open Records Act, which could bemade available online using Open Format data, as well as best practice Open Data structures andformats when possible, that is not Protected Information or Sensitive Information, with no legalrestrictions on use or reuse. Open Data is not information that is treated as exempt under KRS61.878 by Metro Government.OPEN DATA REPORT. The annual report of the Open Data Management Team, which shall (i)summarize and comment on the state of Open Data availability in Metro Government Departmentsfrom the previous year, including, but not limited to, the progress toward achieving the goals of MetroGovernment’s Open Data portal, an assessment of the current scope of compliance, a list of datasetscurrently available on the Open Data portal and a description and publication timeline for datasetsenvisioned to be published on the portal in the following year; and (ii) provide a plan for the next yearto improve online public access to Open Data and maintain data quality.OPEN DATA MANAGEMENT TEAM. A group consisting of representatives from each Departmentwithin Metro Government and chaired by the Data Officer who is responsible for coordinatingimplementation of an Open Data Policy and creating the Open Data Report.DATA COORDINATORS. The members of an Open Data Management Team facilitated by theData Officer and the Office of Civic Innovation and Technology.DEPARTMENT. Any Metro Government department, office, administrative unit, commission, board,advisory committee, or other division of Metro Government.DATA OFFICER. The staff person designated by the city to coordinate and implement the city’sopen data program and policy.DATA. The statistical, factual, quantitative or qualitative information that is maintained or created byor on behalf of Metro Government.DATASET. A named collection of related records, with the collection containing data organized orformatted in a specific or prescribed way.METADATA. Contextual information that makes the Open Data easier to understand and use.OPEN DATA PORTAL. The internet site established and maintained by or on behalf of MetroGovernment located at https://data.louisvilleky.gov/ or its successor website.OPEN FORMAT. Any widely accepted, nonproprietary, searchable, platform-independent, machinereadablemethod for formatting data which permits automated processes.PROTECTED INFORMATION. Any Dataset or portion thereof to which the Department may denyaccess pursuant to any law, rule or regulation.SENSITIVE INFORMATION. Any Data which, if published on the Open Data Portal, could raiseprivacy, confidentiality or security concerns or have the potential to jeopardize public health, safety orwelfare to an extent that is greater than the potential public benefit of publishing that data.§ XXX.02 OPEN DATA PORTAL(A) The Open Data Portal shall serve as the authoritative source for Open Data provided by MetroGovernment.(B) Any Open Data made accessible on Metro Government’s Open Data Portal shall use an OpenFormat.(C) In the event a successor website is used, the Data Officer shall notify the Metro Council andshall provide notice to the public on the main city website.§ XXX.03 OPEN DATA MANAGEMENT TEAM(A) The Data Officer of Metro Government will work with the head of each Department to identify aData Coordinator in each Department. The Open Data Management Team will work to establish arobust, nationally recognized, platform that addresses digital infrastructure and Open Data.(B) The Open Data Management Team will develop an Open Data Policy that will adopt prevailingOpen Format standards for Open Data and develop agreements with regional partners to publish andmaintain Open Data that is open and freely available while respecting exemptions allowed by theKentucky Open Records Act or other federal or state law.§ XXX.04 DEPARTMENT OPEN DATA CATALOGUE(A) Each Department shall retain ownership over the Datasets they submit to the Open DataPortal. The Departments shall also be responsible for all aspects of the quality, integrity and securityPortal. The Departments shall also be responsible for all aspects of the quality, integrity and securityof the Dataset contents, including updating its Data and associated Metadata.(B) Each Department shall be responsible for creating an Open Data catalogue which shall includecomprehensive inventories of information possessed and/or managed by the Department.(C) Each Department’s Open Data catalogue will classify information holdings as currently “public”or “not yet public;” Departments will work with the Office of Civic Innovation and Technology todevelop strategies and timelines for publishing Open Data containing information in a way that iscomplete, reliable and has a high level of detail.§ XXX.05 OPEN DATA REPORT AND POLICY REVIEW(A) Within one year of the effective date of this Ordinance, and thereafter no later than September1 of each year, the Open Data Management Team shall submit to the Mayor and Metro Council anannual Open Data Report.(B) Metro Council may request a specific Department to report on any data or dataset that may bebeneficial or pertinent in implementing policy and legislation.(C) In acknowledgment that technology changes rapidly, in the future, the Open Data Policy shouldshall be reviewed annually and considered for revisions or additions that will continue to positionMetro Government as a leader on issues of

This portal is intended to serve as a means to access all available NMED data via secure Application Programming Interface (API).

To access our data, we require that you register with the portal.

You can browse the titles and a short description of our available APIs before registering.

Anyone who registers will be provided access to all of NMED’s public APIs.

Members of the regulated community served by NMED, as well as partner agencies and other official entities, can, during registration, request an elevated account that provides access to certain protected data APIs. Such requests will need to register using an official email account and potentially provide other means of verification before these account requests are approved.

Once registered, you will be able to fully explore and consume our collection of APIs, and access Open API Specification documentation that will help you understand, learn, and leverage those APIs for your purposes.

We hope you enjoy your visit to the NMED Open Data Portal and return many times.

opendata.swiss is the central portal for open, i.e., freely accessible data from Swiss authorities (Open Government Data, OGD). The opendata.swiss portal provides users with simple and secure central access to open data from the Confederation, cantons, and municipalities. If there is a public interest, data from third parties – federal-affiliated companies and private actors commissioned by federal, cantonal, or municipal authorities – will also be published, even if they are already available on other portals. opendata.swiss is primarily intended to: The portal only contains a catalog of the available data, which is managed by the providers themselves. This avoids duplication. In general, no personal data is published within the framework of OGD. This involves aggregated and anonymized data that does not allow any conclusions to be drawn about individuals. This condition is mandatory and must be adhered to by the data providers. Would you like to publish data on opendata.swiss? Interested providers of public data can find further details in the corresponding handbook under the following link: handbook.opendata.swiss. Who manages the portal? The Open Government Data Office, which is located at the FSO, manages the portal. It supports organizations in the publication of their open data and continuously monitors the quality of the catalog. opendata.swiss was developed as part of the "Strategy for Open Government Data in Switzerland 2019–2023," which the Federal Council adopted on November 30, 2018. This strategic guideline is mandatory for the federal administration. Partner organizations: Until 2018, opendata.swiss was developed under the leadership of the Swiss Federal Archives (SFA) by various partner organizations. In 2019, the Federal Department of Home Affairs (FDHA) transferred responsibility to the Federal Statistical Office (FSO). The portal is continuously being developed in collaboration with the partner organizations. A list of all data providers is available. The following organizations support the OGD Office in the development and administration of the portal: Do you have any questions? Further information and documentation can be found on the website of the Federal Statistical Office (FSO). If you have questions about a specific dataset, please contact the contact point specified for the dataset directly. For general questions about the portal or the topic of public data, we are at your disposal. Translated from German Original Text: opendata.swiss ist das zentrale Portal für offene, d.h. frei zugängliche Daten der Schweizer Behörden (Open Government Data, OGD). Das Portal opendata.swiss gewährleistet den Nutzerinnen und Nutzern einen einfachen und sicheren zentralen Zugang zu den offenen Daten von Bund, Kantonen und Gemeinden. Sofern ein öffentliches Interesse besteht, werden auch Daten von Dritten – bundesnahe Betriebe sowie von den Bundes-, Kantons- oder Gemeindebehörden beauftragte private Akteure – veröffentlicht, selbst wenn sie bereits auf anderen Portalen enthalten sind. opendata.swiss soll in erster Linie: Das Portal enthält lediglich einen Katalog der verfügbaren Daten, die jedoch von den Anbietern selber verwaltet werden. So lassen sich Doppelspurigkeiten vermeiden. Generell werden im Rahmen der OGD keine Personendaten publiziert. Es handelt sich um aggregierte und anonymisierte Daten, die keine Rückschlüsse auf Einzelpersonen zulassen. Diese Bedingung ist zwingend und muss von den Datenanbietern eingehalten werden. Möchten Sie Daten auf opendata.swiss publizieren? Interessierte Anbieter öffentlicher Daten finden weitere Einzelheiten im entsprechenden Handbuch unter folgendem Link: handbook.opendata.swiss. Wer verwaltet das Portal? Die Geschäftsstelle Open Government Data, die beim BFS angesiedelt ist, verwaltet das Portal. Sie unterstützt die Organisationen bei der Publikation ihrer offenen Daten und überwacht fortlaufend die Qualität des Katalogs. opendata.swiss wurde im Rahmen der «Strategie für offene Verwaltungsdaten in der Schweiz 2019–2023», die der Bundesrat am 30. November 2018 verabschiedet hat, entwickelt. Diese strategische Vorgabe ist für die Bundesverwaltung verpflichtend. Partnerorganisationen opendata.swiss wurde bis 2018 unter der Federführung des Schweizerischen Bundesarchivs (BAR) von verschiedenen Partnerorganisationen entwickelt. 2019 hat das Eidgenössische Departement des Innern (EDI) die Verantwortung an das Bundesamt für Statistik (BFS) übertragen. Das Portal wird in Zusammenarbeit mit den Partnerorganisationen laufend weiterentwickelt. Eine Liste aller Datenanbieter steht zur Verfügung. Die nachfolgenden Organisationen unterstützen die OGD-Geschäftsstelle bei der Entwicklung und Verwaltung des Portals: Haben Sie Fragen? Weitere Informationen sowie die Dokumentation finden Sie auf der Website des Bundesamtes für Statistik (BFS). Wenn Sie Fragen zu einem spezifischen Datensatz haben, wenden Sie sich bitte direkt an die für den Datensatz angegebene Kontaktstelle. Bei allgemeinen Fragen zum Portal oder zum Thema öffentliche Daten stehen wir gerne zu Ihrer Verfügung.

IntroductionUK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is the stepping down of voltage as it is moved towards the household; this is achieved using transformers. Transformers have a maximum rating for the utilisation of these assets based upon protection, overcurrent, switch gear, etc. This dataset contains the Grid Substation Transformers, also known as Bulk Supply Points, that typically step-down voltage from 132kV to 33kV (occasionally down to 66 or more rarely 20-25). These transformers can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.Care is taken to protect the private affairs of companies connected to the 33kV network, resulting in the redaction of certain transformers. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.This dataset provides monthly statistics data across these named transformers from 2021 through to the previous month across our license areas. The data are aligned with the same naming convention as the LTDS for improved interoperability.To find half-hourly current and power flow data for a transformer, use the ‘tx_id’ that can be cross referenced in the Grid Transformers Half Hourly Dataset.If you want to download all this data, it is perhaps more convenient from our public sharepoint: Open Data Portal Library - Grid Transformers - All Documents (sharepoint.com)This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.Methodological ApproachThe dataset is not derived, it is the measurements from our network stored in our historian.The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps.We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer.The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation.Quality Control StatementThe data is provided "as is". In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these transformers are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that.Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS transformer from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same transformer in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing transformers, incorrectly labelled transformers, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.Additional informationDefinitions of key terms related to this dataset can be found in the Open Data Portal Glossary.Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information: Open Data Portal Reuses — UK Power Networks

Introduction UK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is distributing this electricity across our regions through circuits. Electricity enters our network through Super Grid Transformers at substations shared with National Grid we call Grid Supply Points. It is then sent at across our 132 kV Circuits towards our grid substations and primary substations. From there, electricity is distributed along the 33 kV circuits to bring it closer to the home. These circuits can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.

This dataset provides half-hourly current and power flow data across these named circuits from 2021 through to the previous month across our Eastern Power Networks (EPN) license area. The data is aligned with the same naming convention as the LTDS for improved interoperability.

Care is taken to protect the private affairs of companies connected to the 33 kV network, resulting in the redaction of certain circuits. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.

To find which circuit you are looking for, use the ‘ltds_line_name’ that can be cross referenced in the 33kV Circuits Monthly Data, which describes by month what circuits were triaged, if they could be made public, and what the monthly statistics are of that site.

If you want to download all this data, it is perhaps more convenient from our public sharepoint: Sharepoint This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets. Methodological Approach The dataset is not derived, it is the measurements from our network stored in our historian. The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps. We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer. The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation. Quality Control Statement The data is provided "as is".
In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these measurements are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that. Assurance Statement Creating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS circuit from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same circuit in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing circuits, incorrectly labelled circuits, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.

Additional InformationDefinitions of key terms related to this dataset can be found in the Open Data Portal Glossary. Download dataset information: Metadata (JSON) We would be grateful if you find this dataset useful to submit a reuse case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information: Open Data Portal Reuses — UK Power Networks

Fishing sectors were established in the Greater Atlantic region in 2010 under catch share management initiatives. Sector data kept at GARFO is mostly a collection of other fishing trip related datasets consolidated into a secure (password protected) web data portal used by sector managers. These datasets would include various trip reports, trip start and end hails, vms declarations, observer reports, etc. The data portal is called SIMM (Sector Information Management Module). There is one component of the data that is not derivative, and that is the sector comparison reports, which help a sector to compare data that they have submitted with data that GARFO has collected.

A resolution of the City Council of the City of Tempe, Arizona, authorizing the mayor to establish an open data program, including an open data policy and open data portal, and authorizing the City Manager to implement an open data program.

Residential Burglary incident Data

Abstract

From 2015, the Department of Basic Education, in collaboration with the University of Witwatersrand and other researchers conducted ongoing research on the acquisition of reading in the early grades in the in South Africa. There have been two Early Grade Reading Studies. This is the restricted-access data from the second study (EGRS II). Information on the restricted-access data from the first study (EGRS I), can be found at https://www.datafirst.uct.ac.za/dataportal/index.php/catalog/1020 The Early Grade Reading Study (EGRS) II. EGRS II was a Randomised Controlled Trial that evaluated two early grade reading interventions in 180 primary schools in two districts in the province of Mpumalanga, South Africa. The interventions focused on early learning of English as a second language The study attempted to measure the causal impact on learner reading performance and investigated the change mechanisms of a structured pedagogic programme. Details of the EGRS II restricted-access data are the following: EGRS II: Language Focus: English first additional language Evaluation Focus: An evaluated intervention to improve reading outcomes in English, the learners' first additional language (EFAL). Two coaching models were employed as part of this intervention: in-person coaching and virtual coaching. The evaluation focused on comparing the impact on literacy outcomes (in both the home language and EFAL) and the cost-effectiveness of these two coaching models Region: Mpumalanga Province (Ehlanzeni District Municipality; Gert Siband District Municipality) Number of Schools: 180 (80 control + 50 in each of the two treatment arms) Data Collection Years: 2017 Waves 1 and 2 (Grade 1 learners); 2018 Wave 3 (Grade 2); 2019 Wave 4 (Grade 3); 2020 Wave 5 (Grade 4) Additional variable available in the restricted-access data: EMIS number of schools in the study NOTE: Only the user guides are provided with the restricted-access data. Researchers can access the relevant documents from the landing-page of the public access dataset.

Analysis unit

Individuals

Kind of data

Qualitative and quantitative data

Mode of data collection

Face-to-face

Research instrument

Data was collected with the following instruments: Learner Assessment forms: A Baseline Learner Assessment form (Grade 1) in Wave 1 A Midline Learner Assessment form (end of Grade 1) in Wave 2 A Grade 2 Learner Assessment form in Wave 3 A Grade 3 Learner Assessment form in Wave 4 A Grade 4 Learner Assessment form in Wave 5

A Parent Questionnaire A Teacher Questionnaire A Principal Questionnaire

The instruments were in English, isiZulu and siSwati. The Study used different Learner Assessment forms each year.The principal and teacher questionnaires were also revised each year.

Secure data sharing with supporting agencies

Encryption technologies are used to secure many applications and websites that you use daily. For example, online banking or shopping, email applications, and secure instant messaging use encryption. Encryption technologies secure information while it is in transit (e.g. connecting to a website) and while it is at rest (e.g. stored in encrypted databases). Many up-to-date operating systems, mobile devices, and cloud services offer built-in encryption, but what is encryption? How is it used? And what should you and your organization consider when using it?

Contains internal files for case processing and information exchanged between agencies and U.S. Office of Personnel Management Federal Investigative Services (OPM-FIS) related to case processing

Abstract

From 2015 the Department of Basic Education in collaboration with the University of Witwatersrand and other researchers conducted ongoing research on the acquisition of reading in the early grades in South Africa. There have been two Early Grade Reading Studies. This is the dataset from the first study (EGRS I) with additional variables shared as restricted access data in a secure remote access data encalve. Information on the restricted access data from the second study (EGRS II), can be found at https://www.datafirst.uct.ac.za/dataportal/index.php/catalog/1021

Details of the EGRS I restricted-access data are as follows: Language Focus: Setswana Evaluation Focus: A comparison of the impact on literacy outcomes and cost-effectiveness of three intervention models (Centralised teacher training, teacher coaching and a parent intervention) to improve reading outcomes in learners’ home language (Setswana) Region: North West Province (Ngaka Modiri Molema District Municipality; Dr Kenneth Kaunda District Municipality) Number of Schools: 230 (80 control + 50 in each of the three treatment arms) Data Collection Years: 2015 Waves 1 and 2 (Grade 1 learners); 2016 Wave 3 (Grade 2); 2018 Wave 4 (Grade 4); 2021 Wave 5 (Grade 7) Restricted-access variables only available in the secure enclave: EMIS number of the schools in the study NOTE: Only the user guide is provided with the restricted-access data. Researchers will need to download the documents from the public access datasets and request these to be added to their account in the secure Remote Access Data Enclave.

Analysis unit

Individuals

Kind of data

Qualitative and quantitative data

Mode of data collection

Face-to-face

Research instrument

Data was collected with learner assessment forms as well as questionnaires for parents, teachers, HODs, and school principals.

The attached file includes the Federal Communication Commission’s Broadband Availability Map Fabric (“Fabric”) Location IDs of unserved locations not in an existing state or federally funded project area. As determined through initial analysis by the Office of Broadband, these 134,221 locations lack broadband access to speeds of 25/3, as set out in the Broadband Equity, Access, and Deployment Program (BEAD). A separate file is also on the Open Data Portal for underserved locations titled “Underserved BEAD Eligible Locations”). These Location IDs correspond with Version 2 of the Fabric. To download the Fabric, eligible entities must execute a license agreement with CostQuest Associates. More information about this process is available at https://www.costquest.com/broadband-serviceable-location-fabric/

Broadband providers must secure a Tier D License from CostQuest to view the statewide fabric. Please visit https://apps.costquest.com/ntiarequest/ for more information.

Local governments and nonprofits must secure a Tier E License from CostQuest to view the statewide fabric. The application for a Tier E License will open soon and this page will be updated accordingly.

These Fabric Location IDs are provided for local governments, non-profit organizations, and internet service providers to analyze for purposes of the upcoming BEAD challenge process, as outlined in Virginia’s BEAD Initial Proposal Volume 1. Please visit the Virginia Telecommunication Initiative website for more information.

Please note the final list of Unserved BEAD Eligible Fabric Location IDs will be published following the BEAD challenge process. Also, please note the Office of Broadband is seeking the authority to publish the physical address which corresponds with each BEAD Eligible Fabric Location ID. This file will be updated as permission is gained or clarified otherwise.

Effective March 7, 2024, the Los Angeles Police Department (LAPD) implemented a new Records Management System aligning with the FBI's National Incident-Based Reporting System (NIBRS) requirements. This switch, part of a nationwide mandate, enhances the granularity and specificity of crime data. You can learn more about NIBRS on the FBI's website here: https://www.fbi.gov/how-we-can-help-you/more-fbi-services-and-information/ucr/nibrs

NIBRS is more comprehensive than the previous Summary Reporting System (SRS) used in the Uniform Crime Reporting (UCR) program. Unlike SRS, which grouped crimes into general categories, NIBRS collects detailed information for each incident, including multiple offenses, offenders, and victims when applicable. This detail-rich format may give the impression of increased crime levels due to its broader capture of criminal activity, but it actually provides a more accurate and nuanced view of crime in our community.

This change sets a new baseline for crime reporting, reflecting incidents in the City of Los Angeles starting from March 7, 2024.

With NIBRS, each criminal incident may reflect multiple offenses, resulting in more robust data than before. This may change the appearance of crime frequency, as multiple offenses per incident are reported individually.

This dataset contains detailed information about UCC filings, typically related to secured transactions and the filing of UCC-1 financing statements. This file serves as a repository for the information related to each individual UCC filing, allowing users to efficiently access, track, and manage UCC filings within the public record system.

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Addresses and Geolocation have been partially obfuscated to the nearest 100 block to protect the privacy of individuals. When mapped this may make the data appear to happen at an intersection when it actually happens within that block. For incidents that do not have an exact block number or the address was typed in by dispatch incorrectly the obfuscation method may place the item much further away than the actual location.

Some incidents will result in a police report, which can be found in the dataset "Police Cases." Case reports involving domestic violence, child abuse, or sex offenses involving minors have been removed from this data set.

Incidents designated as Collisions are included in this dataset and should be classified as what was reported to the 911 dispatcher. In order to track verified collisions a Police Traffic Collision Report (PTCR) is completed, which results in a case being created.