This layer serves as the authoritative geographic data source for California's K-12 public school locations during the 2024-25 academic year. Schools are mapped as point locations and assigned coordinates based on the physical address of the school facility. The school records are enriched with additional demographic and performance variables from the California Department of Education's data collections. These data elements can be visualized and examined geographically to uncover patterns, solve problems and inform education policy decisions.The schools in this file represent a subset of all records contained in the CDE's public school directory database. This subset is restricted to TK-12 public schools that were open in October 2024 to coincide with the official 2024-25 student enrollment counts collected on Fall Census Day in 2024 (first Wednesday in October). This layer also excludes nonpublic nonsectarian schools and district office schools.The CDE's California School Directory provides school location other basic school characteristics found in the layer's attribute table. The school enrollment, demographic and program data are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website. Schools are assigned X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy. Most schools are mapped to the school structure or centroid of the school property parcel and are individually verified using aerial imagery or assessor's parcels databases. Schools are assigned various geographic area values based on their mapped locations including state and federal legislative district identifiers and National Center for Education Statistics (NCES) locale codes.

The NIH Common Data Elements (CDE) Repository has been designed to provide access to structured human and machine-readable definitions of data elements that have been recommended or required by NIH Institutes and Centers and other organizations for use in research and for other purposes. Visit the NIH CDE Resource Portal for contextual information about the repository.

Abstract

DataQuest provides access to a wide variety of reports, including school performance, test results, school staffing, graduation and dropout, and more in California.

Documentation

The California Department of Education (CDE) collects student-level data through the California Longitudinal Pupil Achievement Data System (CALPADS) for state and federal reporting purposes. These data, in addition to assessment data, are available at the aggregate level to the public through the CDE's data reporting portal, DataQuestCDE Downloadable Data Files Web page. PHS has ingested the public and private school listings as well as *the most recent *

• Academic Performance Indicator (API)
• California English Language Development Test (CELDT)
• California High School Exit Exam (CAHSEE)
• Physical Fitness Test (PFT)
• Standardized Testing and Reporting (STAR)
• California Assessment of Student Performance and Progress (CAASPP)

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Visit the DataQuest website for archived performance data.

Unit of analysis

The data include information on the school, district, county, and state levels. Whether a row of data concerns school, district, county, or state data is identified by a record type variable.

Links

• CDE's DataQuest
• Understanding API's
• California Assessment of Student Performance and Progress (CAASPP)

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This layer serves as the authoritative geographic data source for California's K-12 public school locations during the 2023-24 academic year. Schools are mapped as point locations and assigned coordinates based on the physical address of the school facility. The school records are enriched with additional demographic and performance variables from the California Department of Education's data collections. These data elements can be visualized and examined geographically to uncover patterns, solve problems and inform education policy decisions.The schools in this file represent a subset of all records contained in the CDE's public school directory database. This subset is restricted to K-12 public schools that were open in October 2023 to coincide with the official 2023-24 student enrollment counts collected on Fall Census Day in 2023 (first Wednesday in October). This layer also excludes nonpublic nonsectarian schools and district office schools.The CDE's California School Directory provides school location other basic school characteristics found in the layer's attribute table. The school enrollment, demographic and program data are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website. Schools are assigned X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy. Most schools are mapped to the school structure or centroid of the school property parcel and are individually verified using aerial imagery or assessor's parcels databases. Schools are assigned various geographic area values based on their mapped locations including state and federal legislative district identifiers and National Center for Education Statistics (NCES) locale codes.

This layer serves as the authoritative geographic data source for California's K-12 public school locations during the 2022-23 academic year. Schools are mapped as point locations and assigned coordinates based on the physical address of the school facility. The school records are enriched with additional demographic and performance variables from the California Department of Education's data collections. These data elements can be visualized and examined geographically to uncover patterns, solve problems and inform education policy decisions.The schools in this file represent a subset of all records contained in the CDE's public school directory database. This subset is restricted to K-12 public schools that were open in October 2022 to coincide with the official 2022-23 student enrollment counts collected on Fall Census Day in 2022 (first Wednesday in October). This layer also excludes nonpublic nonsectarian schools and district office schools.The CDE's California School Directory provides school location other basic school characteristics found in the layer's attribute table. The school enrollment, demographic and program data are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website. Schools are assigned X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy. Most schools are mapped to the school structure or centroid of the school property parcel and are individually verified using aerial imagery or assessor's parcels databases. Schools are assigned various geographic area values based on their mapped locations including state and federal legislative district identifiers and National Center for Education Statistics (NCES) locale codes.

Brazil Central Government: Expenditure: OE: Financial Support to CDE data was reported at 0.000 BRL mn in Jan 2021. This stayed constant from the previous number of 0.000 BRL mn for Dec 2020. Brazil Central Government: Expenditure: OE: Financial Support to CDE data is updated monthly, averaging 0.000 BRL mn from Jan 1997 (Median) to Jan 2021, with 289 observations. The data reached an all-time high of 2,350.000 BRL mn in Oct 2013 and a record low of 0.000 BRL mn in Jan 2021. Brazil Central Government: Expenditure: OE: Financial Support to CDE data remains active status in CEIC and is reported by National Treasury Secretariat. The data is categorized under Global Database’s Brazil – Table BR.FA003: Central Government Primary Balance. Conta de Desenvolvimento Energético (CDE)

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The vastness of materials space, particularly that which is concerned with metal–organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties.

This layer serves as the authoritative geographic data source for all school district area boundaries in California. School districts are single purpose governmental units that operate schools and provide public educational services to residents within geographically defined areas. Agencies considered school districts that do not use geographically defined service areas to determine enrollment are excluded from this data set. In order to view districts represented as point locations, please see the "California School District Offices" layer. The school districts in this layer are enriched with additional district-level attribute information from the California Department of Education's data collections. These data elements add meaningful statistical and descriptive information that can be visualized and analyzed on a map and used to advance education research or inform decision making.School districts are categorized as either elementary (primary), high (secondary) or unified based on the general grade range of the schools operated by the district. Elementary school districts provide education to the lower grade/age levels and the high school districts provide education to the upper grade/age levels while unified school districts provide education to all grade/age levels in their service areas. Boundaries for the elementary, high and unified school district layers are combined into a single file. The resulting composite layer includes areas of overlapping boundaries since elementary and high school districts each serve a different grade range of students within the same territory. The 'DistrictType' field can be used to filter and display districts separately by type. Boundary lines are maintained by the California Department of Education (CDE) and are effective in the 2024-25 academic year . The CDE works collaboratively with the US Census Bureau to update and maintain boundary information as part of the federal School District Review Program (SDRP). The Census Bureau uses these school district boundaries to develop annual estimates of children in poverty to help the U.S. Department of Education determine the annual allocation of Title I funding to states and school districts. The National Center for Education Statistics (NCES) also uses the school district boundaries to develop a broad collection of district-level demographic estimates from the Census Bureau’s American Community Survey (ACS).The school district enrollment and demographic information are based on student enrollment counts collected on Fall Census Day (first Wednesday in October) in the 2024-25 academic year. These data elements are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website https://www.cde.ca.gov/ds.

Locally defined dataset containing details of all patient data points recorded within the Trust's EHR system. The data points are referred to as DTAs and the list is too long to note here but include every clinical value from data entered against the patients record and pathology results from orders. With recent system upgrade, these also now include Nursing information. Note that this is a very clincially-rich dataset. Items are coded using local Millennium internal codes.

The purpose of the NINDS Common Data Elements (CDEs) Project is to standardize the collection of investigational data in order to facilitate comparison of results across studies and more effectively aggregate information into significant metadata results. The goal of the National Institute of Neurological Disorders and Stroke (NINDS) CDE Project specifically is to develop data standards for clinical research within the neurological community. Central to this Project is the creation of common definitions and data sets so that information (data) is consistently captured and recorded across studies. To harmonize data collected from clinical studies, the NINDS Office of Clinical Research is spearheading the effort to develop CDEs in neuroscience. This Web site outlines these data standards and provides accompanying tools to help investigators and research teams collect and record standardized clinical data. The Institute still encourages creativity and uniqueness by allowing investigators to independently identify and add their own critical variables. The CDEs have been identified through review of the documentation of numerous studies funded by NINDS, review of the literature and regulatory requirements, and review of other Institute''s common data efforts. Other data standards such as those of the Clinical Data Interchange Standards Consortium (CDISC), the Clinical Data Acquisition Standards Harmonization (CDASH) Initiative, ClinicalTrials.gov, the NINDS Genetics Repository, and the NIH Roadmap efforts have also been followed to ensure that the NINDS CDEs are comprehensive and as compatible as possible with those standards. CDEs now available: * General (CDEs that cross diseases) Updated Feb. 2011! * Congenital Muscular Dystrophy * Epilepsy (Updated Sept 2011) * Friedreich''s Ataxia * Parkinson''s Disease * Spinal Cord Injury * Stroke * Traumatic Brain Injury CDEs in development: * Amyotrophic Lateral Sclerosis (Public review Sept 15 through Nov 15) * Frontotemporal Dementia * Headache * Huntington''s Disease * Multiple Sclerosis * Neuromuscular Diseases ** Adult and pediatric working groups are being finalized and these groups will focus on: Duchenne Muscular Dystrophy, Facioscapulohumeral Muscular Dystrophy, Myasthenia Gravis, Myotonic Dystrophy, and Spinal Muscular Atrophy The following tools are available through this portal: * CDE Catalog - includes the universe of all CDEs. Users are able to search the full universe to isolate a subset of the CDEs (e.g., all stroke-specific CDEs, all pediatric epilepsy CDEs, etc.) and download details about those CDEs. * CRF Library - (a.k.a., Library of Case Report Form Modules and Guidelines) contains all the CRF Modules that have been created through the NINDS CDE Project as well as various guideline documents. Users are able to search the library to find CRF Modules and Guidelines of interest. * Form Builder - enables users to start the process of assembling a CRF or form by allowing them to choose the CDEs they would like to include on the form. This tool is intended to assist data managers and database developers to create data dictionaries for their study forms.

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Data about California’s private schools, including directory information, as well as information filed through the annual Private School Affidavit. Although private schools of any size can file an Private School Affidavit (PSA), annual California budget language in Assembly Bill 1464 (Chapter 21, Statutes 2012, Item 6110-001-0001, Provision 1) limits the California Department of Education’s (CDE’s) compiling and sorting of data to schools with six or more students. Therefore, the Private School Directory and Affidavit information contained on this page only includes private schools with enrollments of six or more students reported on their PSA.

This layer serves as the authoritative geographic data source for all school district office locations in California. District office location and attribute information are derived from the California Department of Education's (CDE) public schools and districts directory and district enrollment file.Since the school districts in this layer are represented as point locations instead of service areas, this layer includes additional district types that do not use geographically defined service areas to determine enrollment such as county offices of education, state special schools and State Board of Education (SBE) charter schools. In order to view districts represented as service area polygons, please see the "California School District Areas" layer.The school district enrollment and demographic information are based on the 2022-23 academic year student enrollment counts collected on Fall Census Day in 2022 (first Wednesday in October). These data elements are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website.District records are assigned X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy. Most district offices are mapped to the office structure or centroid of the district office property parcel and are individually verified using aerial imagery or assessor's parcels databases. Districts are assigned various geographic area values based on their mapped locations including state and federal legislative district identifiers and National Center for Education Statistics (NCES) locale codes.

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This web map displays the California Department of Education's (CDE) core set of geographic data layers. This content represents the authoritative source for all statewide public school site locations and school district service areas boundaries for the 2018-19 academic year. The map also includes school and district layers enriched with student demographic and performance information from the California Department of Education's data collections. These data elements add meaningful statistical and descriptive information that can be visualized and analyzed on a map and used to advance education research or inform decision making.

In 2020, the Neurocritical Care Society’s Curing Coma Campaign launched an international initiative to create common data elements (CDEs) for disorders of consciousness (DoC). This CDE initiative is motivated by the recognition that ongoing progress in our field depends on the development of harmonized and uniform data elements. We formed multidisciplinary Work Groups with expertise in 1) Behavioral Phenotyping; 2) Hospital Course/Confounders/Medications; 3) Neuroimaging; 4) Electrophysiology; 5) Biospecimens; 6) Physiologic Data/Big Data; 7) Therapeutic Interventions; 8) Outcomes/Endpoints; and 9) Goals of Care/Family Data. Here, we disseminate the initial recommendations of this CDE development process and version 1.0 of the case report forms (CRFs) with CDEs that can be used in DoC studies. We aim for these CDEs to support progress in the field of DoC research and to facilitate multi-institutional collaboration.

We welcome feedback and are committed to revising the CDEs and CRFs to ensure that they reflect developments in our field. To provide feedback about the current CDEs and CRFs, and to make recommendations about updates for future versions, please email cde.curingcoma@gmail.com.