The following datasets related to regulated drinking water system facilities in California have been developed and are available for the purpose of the March 23, 2023, OEDP workshop. The main purpose of the workshop is to engage community members and researchers in understanding datasets maintained and shared by the California State Water Resources Control Board Drinking Water Program and explore possibilities for their use and enhancement. Input from this workshop could be used to inform recommendations OEDP makes to partnering organizations about how to collect, share, and structure their open datasets. Conversations could also support community organizations in using water datasets to inform programming, policy advocacy, or organizing.

In 2020, nearly one half of the Russian companies stated that the electronic personnel data interchange system was not employed in their enterprises. However,** percent of the polled reported that the roadmap for the implementation has already been developed.

TERN Ecosystem Data Services and Analytics

TERN Research Data Repository

Simplify your research data collection with the help of the research data repository managed by the Terrestrial Ecosystem Research Network. Our collection of ecosystem data includes ecoacustics, bio acoustics, lead area index information and much more.

The TERN research data collection provides analysis-ready environment data that facilitates a wide range of ecological research projects undertaken by established and emerging scientists from Australia and around the world. The resources which we provide support scientific investigation in a wide array of environment and climate research fields along with decision-making initiatives.

Explore Our Ecosystem Data Portals

Open access ecosystem data collections via the TERN Data Discovery Portal and sub-portals:

Access all TERN Environment Data

Discover datasets published by TERN’s observing platforms and collaborators. Search geographically, then browse, query and extract the data via the TERN Data Discovery Portal.

Search EcoPlots data

Search, integrate and access Australia’s plot-based ecology survey data.

Download ausplotsR

Extract, prepare, visualise and analyse TERN Ecosystem Surveillance monitoring data in R.

Search EcoImages

Search and download Leaf Area Index (LAI), Phenocam and Photopoint images.

Explore our data services

Tools that support the discovery, anaylsis and re-use of data:

Visualise the data

We’ve teamed up with ANU to provide 50 landscape and ecosystem datasets presented graphically.

Access CoESRA Virtual Desktop

A virtual desktop environment that enables users to create, execute and share environmental data simulations.

Submit data with SHaRED

Our user friendly tool to upload your data securely to our environment database so you can contribute to Australia’s ecological research.

Other data portals, tools and services

The Soil and Landscape Grid of Australia provides relevant, consistent, comprehensive, nation-wide data in an easily-accessible format. It provides detailed digital maps of the country’s soil and landscape attributes at a finer resolution than ever before in Australia.

The annual Australia’s Environment products summarise a large amount of observations on the trajectory of our natural resources and ecosystems. Use the data explorer to view and download maps, accounts or charts by region and land use type. The website also has national summary reports and report cards for different types of administrative and geographical regions.

TERN’s ausplotsR is an R Studio package for extracting, preparing, visualising and analysing TERN’s Ecosystem Surveillance monitoring data. Users can use the package to directly access plot-based data on vegetation and soils across Australia, with simple function calls to extract the data and merge them into species occurrence matrices for analysis or to calculate things like basal area and fractional cover.

The Australian Cosmic-Ray Neutron Soil Moisture Monitoring Network (CosmOz) delivers soil moisture data for 16 sites over an area of about 30 hectares to depths in the soil of between 10 to 50 cm. In 2020, the CosmOz soil moisture network, which is led by CSIRO, is set to be expanded to 23 sites.

The TERN Mangrove Data Portal provides a diverse range of historical and contemporary remotely-sensed datasets on extent and change of mangrove ecosystems across Australia. It includes multi-scale field measurements of mangrove floristics, structure and biomass, a diverse range of airborne imagery collected since the 1950s, and multispectral and hyperspectral imagery captured by drones, aircraft and satellites.

The TERN Wetlands and Riparian Zones Data Portal provides access to relevant national to local remotely-sensed datasets and also facilitates the collation and collection of on-ground data that support validation.

ecocloud provides easy access to large volumes of curated ecosystem science data and tools, a computing platform and resources and tools for innovative research. ecocloud gives you 10GB of persistent storage to keep your code/notebooks so they are ready to go when you start up a server (R or Python environment). It uses the JupyterLabs interface, which includes connections to GitHub, Google Drive and Dropbox.

Analysis Ready Ecosystem Data

Our research data collection makes it easier for scientists and researchers to investigate and answer their questions by providing them with open data, research and management tools, infrastructure, and site-based research tools.

The TERN data portal provides open access ecosystem data. Our tools support data discovery, analysis, and re-use. The services which we provide facilitate research, education, and management. We maintain a network of monitoring site and sensor data streams for long-term research as part of our research data repository.

The Texas Department of Information Resources (DIR) has established a digital transformation guide to assist agencies with modernizing agency operations and services with respect to electronic data and converting agency information into electronic data. The purpose of the guide is to help Texas government take the next steps to advance digital transformation and improve the customer experience, no matter where the organization is on its digital journey.

Electronic Data Selector Market Outlook

The global market size for Electronic Data Selector was valued at approximately USD 2.5 billion in 2023 and is expected to reach around USD 5.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.5% over the forecast period. The key growth factor driving this market includes the increasing need for efficient data management solutions amid the exponential data growth across various industries.

The primary growth driver for the Electronic Data Selector market is the rapid digitization across multiple sectors, including healthcare, finance, and retail. Companies are increasingly adopting advanced data management solutions to handle vast amounts of data efficiently, thereby boosting the demand for Electronic Data Selectors. The rise in IoT devices and the proliferation of data-generating sources have further elevated the need for robust data management systems, contributing significantly to market growth.

Another crucial factor propelling the market is the growing emphasis on data security and compliance. With stringent data protection regulations such as GDPR and CCPA, organizations are compelled to invest in advanced data selector solutions to ensure compliance. These solutions not only help in managing data efficiently but also ensure that sensitive information is safeguarded, thus driving market adoption across various sectors, including BFSI and healthcare.

Technological advancements in data analytics and AI are also boosting the Electronic Data Selector market. The integration of AI and machine learning within data selector solutions enhances data processing capabilities, allowing for real-time data management and improved decision-making. This technological innovation is attracting investments and encouraging the adoption of advanced data selector solutions, further driving market growth.

Regionally, North America holds a significant share of the Electronic Data Selector market, driven by the presence of key market players and rapid technological advancements. The Asia Pacific region is expected to witness substantial growth due to the increasing digital transformation initiatives and the expanding IT and telecommunications sector. European markets are also poised for growth, supported by stringent data protection regulations and high adoption rates of advanced data management solutions.

Component Analysis

The Electronic Data Selector market by component can be dissected into hardware, software, and services. The hardware segment encompasses various physical components such as servers, storage devices, and networking equipment necessary for the functioning of data selector systems. The rising adoption of advanced hardware solutions that support high-speed data processing and storage requirements is significantly contributing to the growth of this segment. Companies are increasingly investing in robust hardware to enhance the efficiency of their data management systems, driving demand in this segment.

The software segment holds a substantial share in the market and is anticipated to grow at a robust pace. This segment includes data selector applications, data management software, and analytics tools that facilitate efficient data handling. The continuous advancements in software technology, including the integration of AI and machine learning, are enhancing the capabilities of data selector solutions. Such advancements enable real-time data processing, improved decision-making, and enhanced data security, thereby driving the growth of the software segment.

Services form another critical segment in the Electronic Data Selector market. This includes professional services such as consulting, implementation, and maintenance services that support the deployment and ongoing operation of data selector systems. The increasing complexity of data environments and the need for specialized expertise are driving the demand for professional services. Companies are increasingly relying on service providers to ensure the smooth integration and optimal performance of their data selector solutions, contributing to the growth of this segment.

Within the services segment, managed services are gaining traction as organizations look to outsource their data management needs to specialized providers. Managed services offer a cost-effective solution for companies to leverage advanced data selector technologies without the need for significant in-house resources. This trend is particularly prevalent among small and medium enterprises (SMEs), which

This resource collects teaching materials that are originally created for the in-person course 'GEOSC/GEOG 497 – Data Mining in Environmental Sciences' at Penn State University (co-taught by Tao Wen, Susan Brantley, and Alan Taylor) and then refined/revised by Tao Wen to be used in the online teaching module 'Data Science in Earth and Environmental Sciences' hosted on the NSF-sponsored HydroLearn platform.

This resource includes both R Notebooks and Python Jupyter Notebooks to teach the basics of R and Python coding, data analysis and data visualization, as well as building machine learning models in both programming languages by using authentic research data and questions. All of these R/Python scripts can be executed either on the CUAHSI JupyterHub or on your local machine.

This resource is shared under the CC-BY license. Please contact the creator Tao Wen at Syracuse University (twen08@syr.edu) for any questions you have about this resource. If you identify any errors in the files, please contact the creator.

The Information Resources Inc. is a well-established company that specializes in providing a wide range of data related to environmental regulations and policies. As a leading provider of environmental data, the company's primary focus is on collecting and disseminating information related to climate change, emissions, and sustainability. With a vast repository of data, the company's website offers a treasure trove of information on environmental laws, regulations, and standards, making it an invaluable resource for researchers, policymakers, and businesses alike.

From data on greenhouse gas emissions to information on environmental impact assessments, Information Resources Inc. has a vast array of data sets that can be leveraged for research, analysis, and decision-making. With a strong emphasis on quality and accuracy, the company's data is meticulously curated and updated regularly to reflect the latest developments in the industry. Whether you're looking for insights on environmental policy or data on sustainable technologies, Information Resources Inc. is an indispensable resource for anyone working in the environmental sector.

The Geoecology database is a compilation of environmental data for the period 1941 to 1981. The Geoecology database contains selected data on terrain and soils, water resources, forestry, vegetation, agriculture, land use, wildlife, air quality, climate, natural areas, and endangered species. Data on selected human population characteristics are also included to complement the environmental files. Data represent the conterminous United States at the county level. These historical data are provided as a source of 1970s baseline environmental conditions for the United States.

'The California Environmental Resources Evaluation System (CERES) is an program of the California Resources Agency established to facilitate access to a variety of electronic data describing California s rich and diverse environments. The goal of CERES is to improve environmental analysis and planning by integrating natural and cultural resource information from multiple contributors and by making it available and useful to a wide variety of users. CERES collects and integrates data and information and distributes it via the World Wide Web, tapping into important information sources and contributing to advances in the science of data management and metadata cataloging by encouraging cooperation among governmental, educational, and private groups.'

Electronic Data Capture Software Market size was valued at USD 1.37 Billion in 2024 and is projected to reach USD 4.06 Billion by 2031, growing at a CAGR of 14.5% from 2024 to 2031.

Electronic Data Capture Software Market Drivers

Increasing Adoption of Clinical Trials: The global increase in clinical trials in the pharmaceutical and healthcare industries is accelerating the adoption of EDC software, which offers efficient and cost-effective data collection, management, and analysis solutions. Rise in Outsourcing Activities: Pharmaceutical companies and CROs are outsourcing clinical trial activities to save costs, expedite timelines, and access specialized expertise, with EDC software providers providing comprehensive data management solutions. Cost Savings and Efficiency Gains: EDC software significantly reduces costs and improves efficiency by automating data entry, query management, and cleaning processes, thereby reducing time and resources required for clinical data management, thereby accelerating drug and medical device development. Advancements in Technology: Advancements in cloud computing, mobile technology, and AI are driving innovation in EDC software, enabling remote data collection and monitoring, while mobile apps and wearable devices improve clinical trial efficiency and effectiveness. Growing Focus on Patient-Centricity: EDC software enhances patient-centricity in clinical research by enabling remote patient-reported outcomes collection, virtual visits, and decentralized trial management, thereby improving patient convenience and experience.

This list contains useful resources for environmental reporters covering the climate crisis in Africa. It contains links to data source, journalism organisations and training materials.

The NIST Extensible Resource Data Model (NERDm) is a set of schemas for encoding in JSON format metadata that describe digital resources. The variety of digital resources it can describe includes not only digital data sets and collections, but also software, digital services, web sites and portals, and digital twins. It was created to serve as the internal metadata format used by the NIST Public Data Repository and Science Portal to drive rich presentations on the web and to enable discovery; however, it was also designed to enable programmatic access to resources and their metadata by external users. Interoperability was also a key design aim: the schemas are defined using the JSON Schema standard, metadata are encoded as JSON-LD, and their semantics are tied to community ontologies, with an emphasis on DCAT and the US federal Project Open Data (POD) models. Finally, extensibility is also central to its design: the schemas are composed of a central core schema and various extension schemas. New extensions to support richer metadata concepts can be added over time without breaking existing applications. Validation is central to NERDm's extensibility model. Consuming applications should be able to choose which metadata extensions they care to support and ignore terms and extensions they don't support. Furthermore, they should not fail when a NERDm document leverages extensions they don't recognize, even when on-the-fly validation is required. To support this flexibility, the NERDm framework allows documents to declare what extensions are being used and where. We have developed an optional extension to the standard JSON Schema validation (see ejsonschema below) to support flexible validation: while a standard JSON Schema validater can validate a NERDm document against the NERDm core schema, our extension will validate a NERDm document against any recognized extensions and ignore those that are not recognized. The NERDm data model is based around the concept of resource, semantically equivalent to a schema.org Resource, and as in schema.org, there can be different types of resources, such as data sets and software. A NERDm document indicates what types the resource qualifies as via the JSON-LD "@type" property. All NERDm Resources are described by metadata terms from the core NERDm schema; however, different resource types can be described by additional metadata properties (often drawing on particular NERDm extension schemas). A Resource contains Components of various types (including DCAT-defined Distributions) that are considered part of the Resource; specifically, these can include downloadable data files, hierachical data collecitons, links to web sites (like software repositories), software tools, or other NERDm Resources. Through the NERDm extension system, domain-specific metadata can be included at either the resource or component level. The direct semantic and syntactic connections to the DCAT, POD, and schema.org schemas is intended to ensure unambiguous conversion of NERDm documents into those schemas. As of this writing, the Core NERDm schema and its framework stands at version 0.7 and is compatible with the "draft-04" version of JSON Schema. Version 1.0 is projected to be released in 2025. In that release, the NERDm schemas will be updated to the "draft2020" version of JSON Schema. Other improvements will include stronger support for RDF and the Linked Data Platform through its support of JSON-LD.

A list of electronic databases available at Brooklyn Public Library. Update schedule: As required

The Comprehensive Epidemiologic Data Resource (CEDR) is the U.S. Department of Energy’s (DOE) electronic database comprised of health studies of DOE contract workers and environmental studies of areas surrounding DOE facilities. DOE recognizes the benefits of data sharing and supports the public’s right to know about worker and community health risks. CEDR provides independent researchers and the public with access to de-identified data collected since the Department’s early production years. CEDR’s holdings include more than 80 studies of more than one million workers. CEDR is a national user facility, with a large audience for data that are not available elsewhere.

Most of CEDR’s holdings are derived from epidemiologic studies of DOE workers at many large nuclear weapons plants, such as Hanford, Los Alamos, Oak Ridge, Savannah River Site, and Rocky Flats. These studies primarily use death certificate information to identify excess deaths and patterns of disease among workers to determine what factors contribute to the risk of developing cancer and other illnesses. In addition, many of these studies have radiation exposure measurements on individual workers. Other CEDR collections include historical dose reconstruction studies of past offsite radiologic and chemical exposures around the nuclear weapons facilities. Now a mature system in routine operational use, CEDR’s modern, Internet-based systems respond to thousands of requests to its Web server daily.

CEDR’s library of information, reports, journal articles, and data includes nearly 10,000 citations/documents. CEDR’s bibliographic search feature allows the user to select citations or publications associated with the studies found in the CEDR library.

CEDR’s data collection -- There are two types of data derived from epidemiologic studies:

1) Analytic data files: contain the data that a researcher directly used in conducting the analyses and result in reported findings or publication in a peer-reviewed journal. CEDR’s holdings include more than 200 analytic files.

2) Working data files: files that contain the raw or unedited data from which a researcher selected variables to form an initial analytic data file set. The data in the working data files may contain errors; as such, it is recommended that they be analyzed and results interpreted with caution. There are more than 100 working data files in CEDR’s holdings.

US: Bare Land: % of Total Land data was reported at 2.670 % in 2019. This records an increase from the previous number of 2.660 % for 2018. US: Bare Land: % of Total Land data is updated yearly, averaging 2.660 % from Dec 1992 (Median) to 2019, with 5 observations. The data reached an all-time high of 2.670 % in 2019 and a record low of 2.640 % in 2004. US: Bare Land: % of Total Land data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.GGI: Environmental: Land Resources: OECD Member: Annual.

Provide data analysis for environmental element monitoring Air Water resources and marine environ

FDOT District 3Environmental Data ToolThe Environmental Data Tool is designed for the Project Development and Environment (PD&E) phase of the Efficient Transportation Decision Making (ETDM) process. It is intended to assist the environmental review of proposed transportation projects. The data layers in this tool represent the issues and resources addressed in the Preliminary Environmental Discussion (PED) of the PD&E process. The data is organized by these major categories:Natural ResourcesCultural ResourcesPhysical FactorsSocial and Economic FactorsSpecial DesignationsNatural Resources100 Year Flood PlainWetlandsFlorida Managed Areas

Electronic Data Interchange Edi Software Market size was valued at USD 1.89 Billion in 2023 and is expected to reach USD 4.53 Billion by 2031 with a CAGR of 12.3% from 2024-2031.

Global Electronic Data Interchange Edi Software Market Drivers

Digital Transformation: Organizations are increasingly adopting digital technologies to improve operations and enhance efficiency, driving the demand for EDI solutions.

Supply Chain Optimization: EDI helps streamline supply chain operations by facilitating quicker transactions, reducing manual processes, and minimizing errors, leading many companies to invest in EDI systems.

Global Electronic Data Interchange Edi Software Market Restraints

High Implementation Costs: The initial setup and integration of EDI systems can be costly, especially for small and medium-sized enterprises (SMEs) that may lack the budget for such investments. This can deter potential users from adopting EDI solutions.

Complexity of Integration: Integrating EDI software with existing legacy systems and processes can be challenging. Companies often face technical difficulties in merging EDI with their current operations, which might require significant time and resources.

US Fish and Wildlife Service (FWS) Servcat Documents: Topic: Book Chapter

This deposit contains an archive of documents from the US Fish and Wildlife Service (FWS) Servcat system. The documents were obtained by scraping the FWS Servcat system, which is a database of documents related to the management of fish and wildlife resources in the United States. The documents include reports, memos, and other materials related to the management of fish and wildlife resources.

The documents are organized here by general topic, and are contained in a zip file. If the original general topic contained more than 50 Gb of data, the documents are split into multiple zip files. The zip files are named according to the original general topic, and are numbered sequentially when more than one zip file is created. For example, if the original general topic was Geospatial_Dataset, and there were three zip files created, the zip files would be named Geospatial_Dataset_part1.zip, Geospatial_Dataset_part2.zip, and Geospatial_Dataset_part3.zip. If only one zip file is created, it will be named by that general topic, e.g. Geospatial_Dataset.zip.

The main aim of the iCAREdata-project (Improving Care And Research Electronic Data Trust Antwerp) is to develop a central, clinical research database in out-of-hours (OOH) care in Belgium.

With this project, the research team of CHA-ELIZA is developing a state-of-the-art database, in sync with the most recent legal, ethical and privacy aspects present in Belgium and Europe.

One crucial aspect of the project is the unique way it links data between different health care services. Subsequently, we are able to study the chain of care that patients follow in OOH care. This gives a broader view on what is exactly happening with patients suffering an unplanned medical problem.

Weekly results of aggregated data are available at https://icare.uantwerpen.be (Dutch and English)