The Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) facilitates finding and comparing neuroimaging resources for functional and structural neuroimaging analyses—including popular tools as well as those that once might have been hidden in another researcher's laboratory or some obscure corner of cyberspace. NITRC collects and points to standardized information about tools, making the task of finding and comparing them easier than before. The site can help researchers find the right functional or structural neuroimaging tool or resource for their research. NITRC has recently added services such as cloud-based computing and data storage, and is broadening the range of scientific domains from MR to PET, SPECT, CT, MEG/EEG and optical imaging. Additional domains of interest are digital atlasing, and computational neuroscience, including large-scale and multi-scale modeling. The NITRC team searches out relevant research tools and resources to house on the site. Researchers can compare tools on NITRC and developers can seek and receive help from the community to make their tools more usable and accessible.

This dataset includes links and descriptions to a collection of groundwater applications and tools that have been developed by the Department of Water Resources to assist and support groundwater management in California. Each tool or application is included as a separate resource in this dataset and includes a detailed description of the use case and functionality for each tool.

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.

Comprehensive dataset of AI tools, resources, and applications for developers, researchers, and AI enthusiasts

Software repository for comparing structural (MRI) and functional neuroimaging (fMRI, PET, EEG, MEG) software tools and resources. NITRC collects and points to standardized information about structural or functional neuroimaging tool or resource.

The resources within this guide will help you in developing the foundation to write your cover letter and résumé.

Generative AI Tools, Models and Resources is a curated dataset designed to provide an accessible, organized collection of resources in the field of generative artificial intelligence. The dataset is derived from the Awesome Generative AI list (https://github.com/steven2358/awesome-generative-ai) and is available in both CSV and JSON formats. Each resource includes the following fields: Name, URL, description, tags, category, and subcategory.

This dataset is curated by Steven Van Vaerenbergh, a lecturer and researcher in machine learning and mathematics education at the University of Cantabria, Spain. The aim is to provide a practical and well-organized resource for the scientific community. The inclusion criteria reflect a combination of community input and the curator's judgment, making this a selected, rather than exhaustive, collection.

Potential use cases include academic research, teaching, and industry applications for identifying generative AI tools and trends. This repository will be periodically updated, with version history tracked via Zenodo.

License: CC BY 4.0. Proper attribution is required for use of this dataset.

This collection of documents includes Section 508 Surveys and Reports, technical assistance information including the law and regulations, Section 508 Standards, and technical guides.

Talking to family and friends was the most common resource for coping with stress, emotional challenges, or mental health concerns as reported by U.S. veterans and active service members who incurred a physical or mental injury, illness, or wound while serving in the military on or after September 11, 2001 who responded to the annual Wounded Warrior Project Survey in 2022. The Wounded Warrior Project (WWP) is a U.S. charity and veteran organization for veterans and service members who incurred a physical or mental injury, illness, or wound while serving in the military on or after September 11, 2001. This statistic shows the most effective resources for coping with stress, emotional challenges, or mental health concerns as reported by U.S. veterans and active service members of the WWP in 2022.

Data are the foundation of science, and there is an increasing focus on how data can be reused and enhanced to drive scientific discoveries. However, most seemingly “open data” do not provide legal permissions for reuse and redistribution. The inability to integrate and redistribute our collective data resources blocks innovation and stymies the creation of life-improving diagnostic and drug selection tools. To help the biomedical research and research support communities (e.g. libraries, funders, repositories, etc.) understand and navigate the data licensing landscape, the (Re)usable Data Project (RDP) (http://reusabledata.org) assesses the licensing characteristics of data resources and how licensing behaviors impact reuse. We have created a ruleset to determine the reusability of data resources and have applied it to 56 scientific data resources (e.g. databases) to date. The results show significant reuse and interoperability barriers. Inspired by game-changing projects like Creative Commons, the Wikipedia Foundation, and the Free Software movement, we hope to engage the scientific community in the discussion regarding the legal use and reuse of scientific data, including the balance of openness and how to create sustainable data resources in an increasingly competitive environment.

Identifiers represent experimental tools and resources for neurofibromatosis (NF) research in the NF Data Portal Tools catalog.

Introducing a comprehensive and openly accessible dataset designed for researchers and data scientists in the field of artificial intelligence. This dataset encompasses a collection of over 4,000 AI tools, meticulously categorized into more than 50 distinct categories. This valuable resource has been generously shared by its owner, TasticAI, and is freely available for various purposes such as research, benchmarking, market surveys, and more. Dataset Overview: The dataset provides an extensive repository of AI tools, each accompanied by a wealth of information to facilitate your research endeavors. Here is a brief overview of the key components: AI Tool Name: Each AI tool is listed with its name, providing an easy reference point for users to identify specific tools within the dataset. Description: A concise one-line description is provided for each AI tool. This description offers a quick glimpse into the tool's purpose and functionality. AI Tool Category: The dataset is thoughtfully organized into more than 50 distinct categories, ensuring that you can easily locate AI tools that align with your research interests or project needs. Whether you are working on natural language processing, computer vision, machine learning, or other AI subfields, you will find a dedicated category. Images: Visual representation is crucial for understanding and identifying AI tools. To aid your exploration, the dataset includes images associated with each tool, allowing for quick recognition and visual association. Website Links: Accessing more detailed information about a specific AI tool is effortless, as direct links to the tool's respective website or documentation are provided. This feature enables researchers and data scientists to delve deeper into the tools that pique their interest. Utilization and Benefits: This openly shared dataset serves as a valuable resource for various purposes: Research: Researchers can use this dataset to identify AI tools relevant to their studies, facilitating faster literature reviews, comparative analyses, and the exploration of cutting-edge technologies. Benchmarking: The extensive collection of AI tools allows for comprehensive benchmarking, enabling you to evaluate and compare tools within specific categories or across categories. Market Surveys: Data scientists and market analysts can utilize this dataset to gain insights into the AI tool landscape, helping them identify emerging trends and opportunities within the AI market. Educational Purposes: Educators and students can leverage this dataset for teaching and learning about AI tools, their applications, and the categorization of AI technologies. Conclusion: In summary, this openly shared dataset from TasticAI, featuring over 4,000 AI tools categorized into more than 50 categories, represents a valuable asset for researchers, data scientists, and anyone interested in the field of artificial intelligence. Its easy accessibility, detailed information, and versatile applications make it an indispensable resource for advancing AI research, benchmarking, market analysis, and more. Explore the dataset at https://tasticai.com and unlock the potential of this rich collection of AI tools for your projects and studies.

This dataset tracks the updates made on the dataset "Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC)" as a repository for previous versions of the data and metadata.

In the 2023-24 school year, sites and resources were the most accessed educational technology (EdTech) solution for K-12 students and teachers in the United States, at ** percent, followed by supplemental platforms. Supplemental platforms, generally used for individual learning, may be used to access online activities, creation, research, and games to aid learning.

The NASA Technical Standards Program provides access to NASA developed standards and handbooks. The NASA Standards and Technical Assistance Resource Tool, or START, is a Web tool sponsored by the NASA Technical Standards Program. The idea is to make it simpler for technical people to access great NASA technical information quickly and conveniently.

This guide provides an overview of data, tools, and resources related to climate change, including information on the topics of climate, drought, hydrology and fisheries, rangelands, carbon, vulnerability assessments, and monitoring. It includes links to data download sites, story maps, map services, and other resources, and introduces the reader to why these datasets may be helpful for land management planning.

The aim of the database is to give an overview of existing resources, tools and methods to promote crop diversification strategies (rotation, multiple cropping, intercropping) at different levels (including the value chain and territory levels). This version contains 143 resources. Each resource is described with a set of criteria: strategies used / described in the resource, purpose of the resource (what is an end-user doing with the resource), expected performances, area of validity, context of use, but also characteristics for use (cost, training, required time to collect data…). A toolbox was also designed to support end-users to navigate among this database and aims to help different type of end-users to identify interesting and adapted resources to foster crop diversification.

This comprehensive database describes the core attributes of quantification tools developed for market-based conservation in the United States. It encompasses tools designed for compensatory mitigation, non-compensatory mitigation, and voluntary conservation/restoration programs. The dataset consists of 107 tools. Each tool's features are described using 33 attributes related to general, technical, and ecological/geographic details. This database was first published in 2018. Version 2.0 expands upon the original database by including tools developed for compensatory mitigation under the Clean Water Act section 404 regulatory program. Version 2.0 also provides updates on tool details provided in the original database. To access the formatted version of the database that includes supporting information, download Quantification Tools Database--with formatting and supporting materials (ver. 2.0, June 2022).xlsx below. For help understanding the various database files, download the "Gu ...

This database was developed for the Water Availability Tool for Environmental Resources (WATER) for the Delaware River Basin (DRB), a decision support tool that provides a consistent and objective method of simulating streamflow under historical, forecasted, and managed conditions (Williamson and others, 2015). This database provides historical spatial and climatic data for simulating streamflow for 2001–11, in addition to land-cover forecasts and general circulation model (global climate model; GCM) projections that focus on 2030 and 2060. The database provides for geospatial sampling, at a 10-30 m resolution, of landscape characteristics, including topographic and soil properties, land cover and impervious surface, water use, and GCM change factors for precipitation, temperature, and a radiation-based potential evapotranspiration. These data are available as a cohesive unit, that provides the file structure required by the hydrologic tool, in addition to some layers being provided as individual files. Williamson, T.N., Lant, J.G., Claggett, P.R., Nystrom, E.A., Milly, P.C.D., Nelson, H.L., Hoffman, S.A., Colarullo, S.J., and Fischer, J.M., 2015. Summary of hydrologic modeling for the Delaware River Basin using the Water Availability Tool for Environmental Resources (WATER): U.S. Geological Survey Scientific Investigations Report 2015-5143, 68 p., http://doi.org/10.3133/sir20155143.

According to the 2024 Stack Overflow Developer Survey, online resources such as videos, blogs, and forums were the top choice for developers across all age groups worldwide to learn code, with younger developers were more likely to use online sources. The second most popular learning resource for most of the groups were online courses or certifications, which were most popular among those aged 25 to 34 years and 35 to 44 years, with around 54 percent and 52 percent of respondents, respectively. Books and physical media were more popular among developers aged 25 and older compared to younger developers.